Prediction model for resistivity of recycled fine aggregate 3D printing concrete

Abstract

The invention belongs to the field of resistivity prediction of recycled fine aggregate 3D printing concrete in civil engineering, and relates to a resistivity prediction model of recycled fine aggregate 3D printing concrete. The method comprises the following steps: establishing a complex prediction formula through reference resistivity, effective mixing water dosage, effective cementing material, regenerated fine aggregate substitution rate, adhesion old mortar content, three-dimensional porous graphene content, total amount of cementing material, carbon black dosage, 3D printing layer thickness, reference layer thickness, printing speed, reference printing speed, age and reference age; the method has important significance for accurately predicting the resistivity of the recycled fine material 3D printing concrete.

Description

Technical Field

[0001] The invention relates to a resistivity prediction method for 3D printed concrete, and in particular to a resistivity prediction model for 3D printed concrete with recycled fine aggregate. Background Art

[0002] As a key parameter for evaluating the electrical conductivity of concrete materials, concrete resistivity not only directly reflects the combined effect of the internal microstructure and moisture content of concrete, but also plays an important role in structural health monitoring, electromagnetic wave protection, and building material performance optimization. By accurately measuring the resistivity of concrete, we can gain insight into its density, changes in moisture content, and potential structural damage, providing a scientific basis for engineering quality control and safety assessment. At the same time, the unique resistivity characteristics of conductive concrete can also effectively shield electromagnetic interference, opening up a new path for the intelligent and sustainable development of modern buildings.

[0003] Recycled fine aggregate 3D printed concrete (RFA-3DPC) combines the advantages of recycled resource utilization and 3D printing technology, which helps to achieve the sustainable development of building materials. However, the physical properties of recycled fine aggregate (such as high water absorption, large porosity, and high content of attached old mortar) will affect the electrical conductivity of concrete. Adding conductive fillers such as three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) and carbon black (Carbon Black, CB) can significantly improve the electrical properties of concrete and increase its conductivity. This is of great significance for the development of self-sensing concrete, smart structures, etc. Therefore, it is of great significance to establish a complex prediction formula that comprehensively considers the content of three-dimensional porous graphene, carbon black content, recycled fine aggregate content, attached old mortar content, age, and 3D printing process parameters for accurately predicting the resistivity of recycled fine aggregate 3D printed 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 resistivity of recycled fine aggregate 3D printed concrete, which can not only simplify the operation process and shorten the time, but also ensure that the error between the predicted result and the actual measured value 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 resistivity of 3D printed concrete containing recycled fine aggregate, as shown in formula (2):

[0007]

[0008] ρ(t): resistivity of concrete at age t days (Ω·m);

[0009] ρ0: Base resistivity, a constant related to material properties;

[0010] The empirical coefficient of ρ0 is in the range of 330-390; α is the empirical index of the effective water-binder ratio, which is in the range of 0.7-0.9; β is the empirical index of the replacement rate of recycled fine aggregate, which is in the range of 0.3-0.6; γ is the empirical index of the content of attached old mortar, which is in the range of 0.2-0.4; n is the empirical index of the mass percentage of graphene in the cementitious material, which is in the range of 0.5-0.7; m is the empirical index of the mass percentage of carbon black in the cementitious material, which is in the range of 0.6-0.8; k R The range of relevant empirical coefficients is: 0.1~0.3; k M The relevant empirical coefficient ranges from 0.13 to 0.16; k G The experience index ranges from 9 to 11; k CB The experience index ranges from 10 to 13; k L The range of relevant experience index is: 0.04~0.06; k S The range of relevant experience index is: 0.02~0.04; k t The empirical index related to the age ratio ranges from 0.05 to 0.15;

[0011] C eff :Effective cementitious material dosage (kg / m 3 );

[0012] W eff :Effective mixing water consumption (kg / m 3 ), taking into account the water absorption rate of recycled fine aggregate;

[0013] R: Recycled fine aggregate dosage (kg / m 3 );

[0014] A: Total amount of fine aggregate (kg / m 3 ), which is the sum of natural fine aggregate and recycled fine aggregate;

[0015] Recycled fine aggregate replacement rate

[0016] M: content of attached old mortar (%);

[0017] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0018] CB: Carbon black dosage (kg / m 3 );

[0019] B: Total amount of cementitious materials (kg / m 3 );

[0020] t: age (days);

[0021] t0: reference age, generally 28 days;

[0022] L t :3D printing layer thickness (mm);

[0023] L0: reference layer thickness (mm), take the standard value, such as 10mm;

[0024] S: printing speed (mm / s);

[0025] S0: Reference printing speed (mm / s), take the standard value, such as 50mm / s;

[0026] Step 2: Set the reference resistivity ρ0 and the effective cementitious material dosage C eff , effective mixing water consumption W eff , recycled fine aggregate content R, total fine aggregate content A and old mortar content M.

[0027] Step 3: Set the amount of three-dimensional porous graphene G, the amount of carbon black CB, the total amount of gelling material B, and the age t.

[0028] Step 4: Set the reference age t0 and 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0.

[0029] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to predict the resistivity of recycled fine aggregate 3D printed concrete.

[0030] As a further solution of the present invention, the resistivity prediction model of the recycled fine aggregate 3D printing concrete, the effective cementitious material dosage C eff :

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

[0032] C: cement consumption (kg / m 3 );

[0033] F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 );

[0034] k F , k SF , kSL : Activity coefficient of each mineral admixture;

[0035] Effective mixing water consumption W eff :

[0036] W eff =W+R×W abs

[0037] W: Mixing water consumption (kg / m 3 )

[0038] W abs : Water absorption rate of recycled fine aggregate (%), expressed as mass percentage.

[0039] As a further solution of the present invention, the resistivity prediction model of the recycled fine aggregate 3D printing concrete has an effective water-binder ratio range of 0.3 to 0.45 and a mixing water dosage range 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%; Carbon black amount CB: determined by the percentage of the total amount of cementitious materials, such as 0.1%, 0.2%, 0.3%, 0.5%; Set the coarse aggregate replacement rate 0%, 25%, 50%, 75%, 100%; mass percentage (%) of old mortar attached to recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%.

[0040] As a further solution of the present invention, the resistivity prediction model of the recycled fine aggregate 3D printed concrete, three-dimensional porous graphene (G): high-quality, well-dispersed graphene material, carbon black (CB): carbon black material with good conductive properties; 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.

[0041] As a further solution of the present invention, a predictive model for the resistivity of recycled fine aggregate 3D printed concrete is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material, fine aggregate, graphene and carbon black are mixed evenly; and (2) wet mixing: effective mixing water and admixtures are added, and the mixture is stirred evenly to ensure that the graphene and carbon black are fully dispersed.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) The present invention can quickly predict the resistivity of recycled fine aggregate 3D printed concrete by using the baseline resistivity, three-dimensional porous graphene content, carbon black content, recycled fine aggregate content, attached old mortar content, age and 3D printing process parameters.

[0044] (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

[0045] The specific embodiments of the present invention will be described in detail and comprehensively below to illustrate its technical solution. It is important to point out that the embodiments described here only represent some examples of the present invention and do not cover all possible implementation forms. Based on these examples, any other implementation forms that can be derived by those skilled in the art without performing creative work should be deemed to fall within the scope of protection of the present invention.

[0046] In this specific embodiment, the raw materials for producing the recycled fine aggregate 3D printing concrete include cement, fly ash, slag, silica fume, three-dimensional porous graphene, fine aggregate and natural fine aggregate. In addition, the recycled fine aggregate 3D printing concrete is not limited to the above raw materials during the specific implementation of the present invention. Among the above raw materials, cement is ordinary silicate cement, and the fine aggregate is medium sand. The recycled fine aggregate is mixed with the natural fine aggregate, and the grade is matched.

[0047] Five groups of tests and verifications of the resistivity of 3D printed concrete with recycled fine aggregate were carried out. The proportion of concrete raw materials is shown in Table 1:

[0048] Table 1

[0049]

[0050] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0051] By formula:

[0052]

[0053] The calculated predicted values ​​(in Ω·m) are compared with the measured values ​​(in Ω·m) at 28 days, as shown in Table 2:

[0054] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 200 195 5 2.50 0.975 2 186 184 2 1.08 0.989 3 165 163 2 1.21 0.988 4 150 148 2 1.33 0.987 5 120 118 2 1.67 0.983

[0055] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention has an error control within 5Ω·m when predicting the resistivity of concrete, with the minimum error being only 2Ω·m, the maximum error being 5Ω·m, and the average error reaching 2.6Ω·m. More importantly, the average relative error is only 1.56%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 0.975 to 0.989, showing an accuracy rate of more than 95%. These data fully prove that the prediction formula in the present invention not only has a high correlation when calculating the resistivity of concrete at 28d, but also has excellent prediction accuracy and precision.

[0056] 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 resistivity of recycled fine aggregate 3D printed concrete, characterized in that The following steps are involved: Step 1: Establish a prediction model for the resistivity of 3D printed concrete containing recycled fine aggregate, as shown in formula (1): ρ(t): resistivity of concrete at age t days (Ω·m); ρ0: Base resistivity, a constant related to material properties; The empirical coefficient of ρ0 is in the range of 330-390; the empirical index of α related to the effective water-binder ratio is in the range of: 0.7~0.9; 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 cementitious materials, ranging from 0.5 to 0.7; m is an empirical index related to the mass percentage of carbon black in cementitious materials, ranging from 0.6 to 0.8; k R The range of relevant empirical coefficients is: 0.1~0.3; k M The relevant empirical coefficient ranges from 0.13 to 0.16; k G The experience index ranges from 9 to 11; k CB The experience index ranges from 10 to 13; k L The range of relevant experience index is: 0.04~0.06; k S The range of relevant experience index is: 0.02~0.04; k t The empirical index related to the age ratio ranges from 0.05 to 0.15; C eff : Effective amount of cementitious material (kg / m3); W eff : Effective mixing water consumption (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; Recycled fine aggregate replacement rate M: content of attached old mortar (%); G: three-dimensional porous graphene dosage (kg / m3); CB: Carbon black 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 reference resistivity ρ0 and the effective cementitious material dosage C eff , effective mixing water consumption W eff , recycled fine aggregate dosage R, total fine aggregate amount A and old mortar content M; Step 3, setting the amount of three-dimensional porous graphene G, the amount of carbon black CB, the total amount of gelling material B, and the age t; Step 4: Set the reference age t0 and 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 obtain a prediction model for the resistivity of recycled fine aggregate 3D printed concrete.

2. A prediction model for the resistivity of recycled fine aggregate 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 (%), expressed as mass percentage.

3. A prediction model for resistivity of recycled fine aggregate 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 amount of carbon black CB: determined by the percentage of the total amount of cementitious materials, such as 0.1%, 0.2%, 0.3%, 0.5%; set the fine aggregate replacement rate 0%, 25%, 50%, 75%, 100%; mass percentage (%) of old mortar attached to recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%.

4. A prediction model for resistivity of recycled fine aggregate 3D printed concrete according to claim 1, characterized in that: In step 1, three-dimensional porous graphene (G): high-quality, well-dispersed graphene material, carbon black (CB): carbon black material with good conductive properties; 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 resistivity of recycled fine aggregate 3D printed concrete according to claim 1, characterized in that: Material mixing: (1) Dry mixing: Mix the cementitious material, fine aggregate, graphene and carbon black evenly; (2) Wet mixing: Add effective mixing water and admixtures, stir evenly to ensure that the graphene and carbon black are fully dispersed.

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