A calculation method of a chloride ion diffusion 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 chloride ion diffusion prediction, and relates to a calculation method of a recycled coarse aggregate 3D printing concrete chloride ion diffusion prediction model. A complex prediction formula is established by using a reference chloride ion diffusion coefficient, an effective mixing water amount, effective cementitious materials, a recycled coarse aggregate replacement rate, three-dimensional porous graphene content, total cementitious materials, a mass percentage of attached old mortar in the recycled coarse aggregate, a 3D printing layer thickness, a reference layer thickness, a printing speed, a reference printing speed, an age, and a reference age, which is of great significance for accurately predicting the chloride ion diffusion of the recycled coarse aggregate 3D printing concrete.

Description

TECHNICAL FIELD

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

[0002] Chloride ion erosion is one of the main causes of durability problems in reinforced concrete structures. When chloride ions penetrate the surface of steel bars through the pores and cracks of concrete, they can cause corrosion of the steel bars, which in turn leads to deterioration of the structural performance. The chloride ion diffusion coefficient is an important parameter for measuring the resistance of concrete to chloride ion erosion, and directly affects the service life of the structure.

[0003] Recycled coarse aggregate concrete (RCAC) has a higher internal porosity and complex pore structure due to the use of recycled coarse aggregate, which makes it easier for chloride ions to penetrate. At the same time, the increase in the amount of attached old mortar further deteriorates the durability of the concrete. Therefore, the introduction of three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) can improve the microstructure of the concrete, fill the pores, and improve the resistance to chloride ion erosion. Combined with 3D printing technology, the forming process and structure of the concrete can be precisely controlled, improving the utilization efficiency and performance of the material. 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 chloride ion diffusion of recycled coarse aggregate 3D printed concrete that simplifies the operation process, shortens the time consumption, and ensures that the prediction results have a small error and high accuracy compared to actual measurements.

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

[0006] Step 1, establish a prediction model for chloride ion diffusion of recycled coarse aggregate 3D printed concrete, as shown in formula (2);

[0007]

[0008] D C1 (t): chloride ion diffusion coefficient at age t days, unit ×10 -12 m 2 / s;

[0009] D0: reference chloride ion diffusion coefficient, unit ×10 -12 m 2 / s, a constant related to material properties;

[0010] D0 is an empirical coefficient related to data fitting, ranging from (19-25) x 10 -12 ; a is an empirical index related to effective water-binder ratio, ranging from 0.7-0.9; b is an empirical index related to recycled coarse aggregate replacement rate, ranging from 0.4-0.6; d is an empirical index related to attached old mortar content, ranging from 0.2-0.4; n is an empirical index related to mass percentage of graphene in cementitious materials, ranging from 0.4-0.6; k R is an empirical coefficient related to data fitting, ranging from 0.2-0.4; k M is an empirical coefficient related to data fitting, ranging from 0.1-0.3; k G is an empirical index related to data fitting, ranging from 14-16; k L is an empirical index related to data fitting, ranging from 0.04-0.06; k S is an empirical index related to data fitting, ranging from 0.02-0.04; k t is an empirical index related to data fitting, ranging from 0.1-0.3;

[0011] C eff : Effective cementitious material dosage, unit: kg / m 3 ;

[0012] W eff : Effective mixing water dosage, unit: kg / m 3 , considering the water absorption of recycled coarse aggregate and attached old mortar;

[0013] R: Recycled coarse aggregate dosage, unit: kg / m 3 ;

[0014] G t : Total coarse aggregate amount, unit: kg / m 3 , i.e. the sum of natural coarse aggregate and recycled coarse aggregate;

[0015] M: Mass percentage of attached old mortar in recycled coarse aggregate;

[0016] G: Three-dimensional porous graphene dosage, unit: kg / m 3 ;

[0017] B: Total cementitious material amount, 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, take standard value;

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

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

[0024] Step 2, set the quasi-reference chloride ion diffusion coefficient D0, effective cementitious material dosage C eff , effective mixing water W eff , the replacement rate of the amount of recycled coarse aggregate The mass percentage M of the attached old mortar in the recycled coarse aggregate.

[0025] 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.

[0026] 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.

[0027] Step 5, substitute the relevant parameters in step 2, step 3 and step 4 into formula (2) to realize the prediction of chloride ion diffusion of recycled coarse aggregate 3D printing concrete.

[0028] As a further scheme of the application, the calculation method of the recycled coarse aggregate 3D printing concrete chloride ion diffusion prediction model, the effective cementitious material dosage C eff :

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

[0030] C: cement dosage, unit: kg / m3;

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

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

[0033] Effective mixing water W eff :

[0034] W eff =W+R×W abs +R×M×W old

[0035] W: Mixing water consumption, in kg / m3;

[0036] W abs The water absorption rate of recycled fine aggregate is expressed as a percentage by mass.

[0037] W old Water absorption rate of the attached old mortar, expressed as a percentage by mass.

[0038] As a further aspect of the present invention, the calculation method for the chloride ion diffusion prediction model of 3D printed concrete using recycled coarse aggregate has an effective water-cement ratio ranging from 0.3 to 0.45; and a mixing water dosage ranging 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%; the amount of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious materials; the coarse aggregate replacement rate is set. 0%, 25%, 50%, 75%, 100%; the mass percentage of old mortar adhering to recycled coarse aggregate, M: 0%, 10%, 15%, 20%, 25%.

[0039] As a further aspect of this invention, a calculation method for a chloride ion diffusion prediction model of 3D-printed concrete using recycled coarse aggregate is described, with a 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.

[0040] As a further embodiment of the present invention, the calculation method of the chloride ion diffusion prediction model of 3D printed concrete with recycled coarse aggregate includes the following material mixing: (1) Dry mixing: the graphene-containing cementitious material, natural coarse aggregate, and recycled coarse aggregate are mixed evenly; (2) Wet mixing: the natural coarse aggregate and recycled coarse aggregate are mixed with effective mixing water and admixtures, and stirred evenly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] (1) The present application can quickly predict the chloride ion diffusion of the recycled coarse aggregate 3D printed concrete by using the reference chloride ion diffusion coefficient, the three-dimensional porous graphene content, the recycled coarse aggregate content, the attached old mortar content, the age, and the 3D printing process parameters.

[0043] (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

[0044] The specific embodiments of the present application will be introduced in depth and comprehensively below, and the technical solutions thereof will be described in detail. It should be noted that the embodiments listed here are only a part of the numerous examples of the present application, and do not cover all possible implementation manners. Based on these specific embodiments, all other implementation forms reasonably deduced by those skilled in the art without additional creative efforts should be considered to fall within the protection scope of the claims of the present application.

[0045] In the present specific embodiment, the recycled coarse aggregate 3D printed concrete production raw materials include cement, fly ash, slag, silica fume, three-dimensional porous graphene, recycled coarse aggregate, and natural coarse aggregate, and in addition, the recycled coarse aggregate 3D printed 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, and the recycled coarse aggregate is mixed with the natural coarse aggregate for use, and the gradation is qualified.

[0046] Five groups of recycled coarse aggregate 3D printed concrete tests and verifications are carried out, and the concrete raw material mix proportions are as shown in Table 1:

[0047] Table 1

[0048]

[0049] 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.

[0050] Through the formula:

[0051]

[0052] The predicted value (unit x 10 -12 ) calculated is compared with the actual measured value (unit x 10 -12 ) at 28d, and the comparison is as follows:

[0053] Table 2:

[0054] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 ]]> 1 12.4 12.1 0.3 2.42 0.976 2 10.2 9.9 0.3 2.94 0.971 3 7.2 6.9 0.3 4.17 0.958 4 6.2 6.0 0.2 3.23 0.968 5 4.1 3.9 0.2 4.88 0.951

[0055] Through the deep analysis of the data in Table 2, we found that the prediction formula in the present application in predicting the concrete resistivity, the error between the measured value is controlled within 0.3x10 -12 , wherein the minimum error is only 0.2x10 -12 , the maximum error is 0.3x10 -12 , and the average error reaches 0.26x10 -12 . More noteworthy is that the average relative error is only 3.53%, and the ratio of the predicted value to the measured value (Ypredicted / Ymeasured) is stable in the range of 0.951-0.976, showing an accuracy of up to 95%. These data fully prove that the prediction formula in the present application in calculating the 28d chloride ion diffusion of concrete not only has a high degree of correlation, but also shows excellent prediction accuracy and accuracy.

[0056] 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, those skilled in the art 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 by the appended claims.

Claims

1. A computing method of a chloride ion diffusion prediction model of recycled coarse aggregate 3D printed concrete, characterized by The method comprises the following steps: Step 1, establishing a prediction model of chloride ion diffusion of recycled coarse aggregate 3D printing concrete, as shown in formula (1) D C1 (t): Chloride diffusion coefficient at age t, in days, in units of x 10 -12 m 2 / s; D0: reference chloride diffusion coefficient in x 10 -12 m 2 / s, constant related to material properties; D0 is an empirical coefficient related to data fitting, which ranges from (19-25) x 10 -12 ; a is an empirical index related to effective water-binder ratio, which ranges from 0.7-0.9; b is an empirical index related to recycled coarse aggregate replacement rate, which ranges from 0.4-0.6; d is an empirical index related to attached old mortar content, which ranges from 0.2-0.4; n is an empirical index related to the mass percentage of graphene in cementitious materials, which ranges from 0.4-0.6; k R is an empirical coefficient related to data fitting, which ranges from 0.2-0.4; k M is an empirical coefficient related to data fitting, which ranges from 0.1-0.3; k G is an empirical index related to data fitting, which ranges from 14-16; k L is an empirical index related to data fitting, which ranges from 0.04-0.06; k S is an empirical index related to data fitting, which ranges from 0.02-0.04; k t is an empirical index related to data fitting, which ranges from 0.1-0.3; C eff : Effective gelling material amount, in kg / m 3 ; W eff : Effective water quantity for mixing, in kg / m 3 , taking into account the water absorption of the recycled coarse aggregate and of the adhering old mortar R: Recycled coarse 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 old mortar attached in the recycled coarse aggregate; G: amount of three-dimensional porous graphene, 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, setting the reference chloride ion diffusion coefficient D0; effective cementitious material amount C eff and effective mixing water amount W eff ; recycled coarse aggregate amount replacement rate Mass percentage M of the attached old mortar in the recycled coarse 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 step 2, step 3 and step 4 into formula (1), the prediction model of chloride ion diffusion 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 fine 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-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%; the amount of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious materials; the coarse aggregate replacement rate is set. 0%, 25%, 50%, 75%, 100%; The mass percentage M of the old mortar attached in the recycled coarse aggregate is 0, 10%, 15%, 20% and 25%. Material mixing: (1) dry mixing: mixing the cementitious materials containing graphene, natural coarse aggregate and recycled coarse aggregate uniformly; (2) wet mixing: adding effective mixing water and admixture to the natural coarse aggregate and recycled coarse aggregate, and stirring uniformly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

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: ​

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