Prediction model for elastic modulus of recycled coarse aggregate 3D printing concrete

Abstract

The invention belongs to the field of elasticity modulus prediction of recycled coarse aggregate 3D printing concrete in civil engineering, and relates to a prediction model for the elasticity modulus of recycled coarse aggregate 3D printing concrete. A complex prediction formula is established through the concrete density, the compressive strength, the replacement rate of the recycled coarse aggregate, the content of the three-dimensional porous graphene, the content of the attached old mortar, the age and the 3D printing layer parameters, and the method has important significance on accurate prediction of the elastic modulus of the recycled coarse aggregate 3D printing concrete.

Description

Technical Field

[0001] The present invention relates to a method for predicting the elastic modulus of 3D printed concrete, and in particular to a prediction model for the elastic modulus of 3D printed concrete with recycled coarse aggregate. Background Art

[0002] The elastic modulus is an important mechanical parameter that measures the deformation capacity of concrete materials under stress, and is of great significance for structural design and performance analysis. Recycled coarse aggregate concrete (RCAC) uses recycled coarse aggregate, which has problems such as high water absorption, large porosity, and weak interface transition zone, resulting in a low elastic modulus. In addition, the content of attached old mortar in the recycled coarse aggregate will further affect the elastic modulus of concrete.

[0003] In order to improve the performance of recycled coarse aggregate concrete, 3D-Porous Graphene (3D-PG), as a new type of nanomaterial, has been applied to the research of reinforced concrete due to its excellent mechanical properties and nanostructure. 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, it is of great significance to establish a complex prediction formula that comprehensively considers the content of 3D porous graphene, recycled coarse aggregate content, attached old mortar content, age, and 3D printing process parameters for accurately predicting the elastic modulus of 3D printed recycled coarse 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 measuring the elastic modulus of recycled coarse 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 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 elastic modulus of 3D printed concrete containing recycled coarse aggregate, as shown in formula (2):

[0007]

[0008] Among them, the elastic modulus related terms are:

[0009] E c (t): elastic modulus at age t days (GPa);

[0010] E c0 : Reference elastic modulus, elastic modulus at reference age to (GPa);

[0011] ρ c : Actual density of concrete (kg / m 3 );

[0012] ρ0: Base density, ordinary concrete density is 2400kg / m 3 ;

[0013] k1: density influence index;

[0014] f c (t): compressive strength at age t days (MPa);

[0015]

[0016] f c0 : Benchmark compressive strength, compressive strength at reference age to (MPa);

[0017] k2: compressive strength influence index;

[0018] C eff :Effective cementitious material dosage (kg / m 3 );

[0019] W eff :Effective mixing water consumption (kg / m 3 ), taking into account the water absorption of recycled coarse aggregate and attached old mortar;

[0020] k' related empirical coefficient ranges from 14 to 19; γ and the effective glue ratio related empirical index ranges from 0.8 to 1.0; k R 'The relevant empirical coefficient ranges from 0.3 to 0.5; δ is the empirical index related to the replacement rate of recycled coarse aggregate, which ranges from 0.3 to 0.6; k M 'The relevant empirical coefficient ranges from 0.24 to 0.26; ε is the empirical index related to the content of the attached old mortar, which ranges from 0.2 to 0.4; k G 'The relevant empirical coefficient ranges from 17 to 19; m The empirical index related to the mass percentage of graphene in the gelling material ranges from 0.5 to 0.7; k t ' is an empirical index related to the age ratio, ranging from 0.2 to 0.3; k1 is an empirical index related to the ratio of actual density to reference density, ranging from 1.4 to 1.6; k2 is an empirical index related to the ratio of t-day to 28-day reference compressive strength, ranging from 0.2 to 0.4; k G The relevant empirical coefficient ranges from 12 to 19; n is the empirical index of the mass percentage of graphene in the cementitious material, which ranges from 0.4 to 0.7; k LThe range of relevant experience index is: 0.04~0.06; k S The relevant empirical index ranges from 0.02 to 0.04; α is the empirical index related to the replacement rate of recycled coarse aggregate, which ranges from 0.4 to 0.6; β is the empirical index related to the content of attached old mortar, which ranges from 0.2 to 0.4; k R The range of relevant empirical coefficients is: 0.30~0.40; k M The relevant empirical coefficient ranges from 0.24 to 0.26; the reference elastic modulus E c0 The range of relevant empirical coefficients is: 25~35GPa;

[0021] Influence of recycled coarse aggregate:

[0022] R: Recycled coarse aggregate dosage (kg / m 3 );

[0023] G t :Total amount of coarse aggregate (kg / m 3 ), which is the sum of natural coarse aggregate and recycled coarse aggregate;

[0024] Recycled coarse aggregate replacement rate;

[0025] k R , α: coefficient and index reflecting the influence of recycled coarse aggregate on elastic modulus;

[0026] M: content of attached old mortar (%), mass percentage of attached old mortar in recycled coarse aggregate;

[0027] k M , β: coefficient and index reflecting the influence of attached old mortar on elastic modulus;

[0028] Impact of three-dimensional porous graphene:

[0029] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0030] B: Total amount of cementitious materials (kg / m 3 );

[0031] The mass percentage of graphene in the cementitious material;

[0032] k G , n: coefficient and index reflecting the graphene enhancement effect;

[0033] 3D printing process parameters affecting:

[0034] L t :3D printing layer thickness (mm);

[0035] L0: reference layer thickness (mm), take the standard value, such as 10mm;

[0036] S: printing speed (mm / s);

[0037] S0: Reference printing speed (mm / s), take the standard value, such as 50mm / s;

[0038] k L , k S : Coefficient reflecting the influence of layer thickness and printing speed on elastic modulus;

[0039] Step 2: Set the reference elastic modulus E c0 , actual density of concrete ρ c and the base density ρ0, the compressive strength f at age t c (t), baseline compressive strength f c0 The mass percentage of old mortar attached to the recycled coarse aggregate M and the replacement rate of recycled coarse aggregate Effective cementitious material dosage C eff and effective cementitious material dosage W eff .

[0040] 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 t0.

[0041] Step 4: Set the 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0.

[0042] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (2) to predict the elastic modulus of recycled coarse aggregate 3D printed concrete.

[0043] As a further solution of the present invention, the prediction model of elastic modulus of recycled coarse aggregate 3D printing concrete, the effective cementitious material dosage C eff :

[0044] C eff =C+k F F+k SF SF+k SL SL

[0045] C: cement consumption (kg / m3);

[0046] F, SF, SL: the amount of fly ash, silica ash, and slag powder (kg / m 3 );

[0047] k F , k SF , k SL: Activity coefficient of each mineral admixture;

[0048] Effective mixing water consumption W eff :

[0049] W eff =W+R×W abs +R×M×W old

[0050] W: Mixing water consumption (kg / m 3 );

[0051] W abs : Water absorption of recycled fine aggregate (%), expressed as mass percentage;

[0052] W old : Water absorption rate of attached old mortar (%), expressed as mass percentage.

[0053] As a further solution of the present invention, the prediction model of elastic modulus of recycled coarse 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%; set the coarse aggregate replacement rate 0%, 25%, 50%, 75%, 100%; Reference elastic modulus E c0 :25~35GPa; actual density of concrete ρ c : Determined based on the mass and volume of the test block; the mass percentage (%) of old mortar attached to the recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%.

[0054] As a further solution of the present invention, the 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.

[0055] As a further solution of the present invention, a prediction model for the elastic modulus of 3D printed concrete with recycled coarse aggregate is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material, aggregate and graphene are mixed evenly; and (2) wet mixing: natural coarse aggregate and recycled coarse aggregate are added with effective mixing water and admixtures, and stirred evenly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) The present invention utilizes a complex prediction formula of three-dimensional porous graphene content, recycled coarse aggregate replacement rate, attached old mortar content, density, compressive strength, age and 3D printing process parameters to accurately predict the elastic modulus of 3D printed recycled coarse aggregate concrete.

[0058] (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

[0059] The specific embodiments of the present invention will be introduced in depth and comprehensively below, and the technical solutions therein will be elaborated in detail. It should be clear that the embodiments listed here are only a part of the many examples of the present invention and do not cover all possible implementation methods. Based on these specific embodiments, all other implementation forms that can be reasonably derived by those skilled in the art without additional creative efforts should be deemed to fall within the scope of protection of the claims of the present invention.

[0060] In this 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 is not limited to the above raw materials when the present invention is implemented. Among the above raw materials, cement is ordinary silicate cement, and recycled coarse aggregate is mixed with natural coarse aggregate, and the grade is matched.

[0061] Five groups of recycled coarse aggregate 3D printing concrete tests and verifications were carried out, and the concrete raw material mix ratio is shown in Table 1:

[0062] Table 1

[0063]

[0064] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0065] By formula:

[0066]

[0067] The calculated predicted values ​​(in GPa) are compared with the measured values ​​(in GPa) at 28 days, as shown in Table 2:

[0068] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 23.9 23.4 0.5 2.09 0.979 2 22 21.7 0.3 1.36 0.986 3 23.6 23.1 0.5 2.12 0.979 4 24.2 23.7 0.5 2.07 0.979 5 26.3 26.1 0.2 0.76 0.992

[0069] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 0.5GPa when predicting the concrete resistivity, and the error between the actual value and the actual value is controlled within 0.5GPa, of which the minimum error is only 0.2GPa, the maximum error is 0.5GPa, and the average error reaches 0.4GPa. It is worth mentioning that the average relative error is only 1.68%, and the ratio of the predicted value to the actual value (Y predicted / Y measured) is stable in the range of 0.979 to 0.992, 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 elastic modulus of concrete, but also has excellent prediction accuracy and precision.

[0070] 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 elastic modulus of recycled coarse aggregate 3D printed concrete, characterized in that The method comprises the following steps: Step 1: Establish a prediction model for the elastic modulus of 3D printed concrete containing recycled coarse aggregate, as shown in formula (1): Among them, the elastic modulus related terms are: E c (t): elastic modulus at age t days (GPa); E c0 : Reference elastic modulus, elastic modulus at reference age to (GPa); ρ c : Actual density of concrete (kg / m 3 ); ρ0: Base density, ordinary concrete density is 2400kg / m 3 ; k1: density influence index; f c (t): compressive strength at age t days (MPa); f c0 : Benchmark compressive strength, compressive strength at reference age to (MPa); k2: compressive strength influence index; C eff :Effective cementitious material dosage (kg / m 3 ); W eff :Effective mixing water consumption (kg / m 3 ), taking into account the water absorption of recycled coarse aggregate and attached old mortar; k' related empirical coefficient ranges from 14 to 19; γ is related to the effective glue ratio and its range is 0.8 to 1.0; k R 'The relevant empirical coefficient ranges from 0.3 to 0.5; δ is the empirical index related to the replacement rate of recycled coarse aggregate, which ranges from 0.3 to 0.6; k M 'The relevant empirical coefficient ranges from 0.24 to 0.26; ε is the empirical index related to the content of the attached old mortar, which ranges from 0.2 to 0.4; k G 'The relevant empirical coefficient ranges from 17 to 19; m The empirical index related to the mass percentage of graphene in the gelling material ranges from 0.5 to 0.7; k t ' is an empirical index related to the age ratio, ranging from 0.2 to 0.3; k1 is an empirical index related to the ratio of actual density to reference density, ranging from 1.4 to 1.6; k2 is an empirical index related to the ratio of t-day to 28-day reference compressive strength, ranging from 0.2 to 0.4; k G The relevant empirical coefficient ranges from 12 to 19; n is the empirical index of the mass percentage of graphene in the cementitious material, which ranges from 0.4 to 0.7; k L The range of relevant experience index is: 0.04~0.06; k S The relevant empirical index ranges from 0.02 to 0.04; α is the empirical index related to the replacement rate of recycled coarse aggregate, which ranges from 0.4 to 0.6; β is the empirical index related to the content of attached old mortar, which ranges from 0.2 to 0.4; k R The range of relevant empirical coefficients is: 0.30~0.40; k M The relevant empirical coefficient ranges from 0.24 to 0.26; the reference elastic modulus E c0 The range of relevant empirical coefficients is: 25~35GPa; Influence of recycled coarse aggregate: R: Recycled coarse aggregate dosage (kg / m 3 ); G t :Total amount of coarse aggregate (kg / m 3 ), which is the sum of natural coarse aggregate and recycled coarse aggregate; Recycled coarse aggregate replacement rate; k R , α: coefficient and index reflecting the influence of recycled coarse aggregate on elastic modulus; M: content of attached old mortar (%), mass percentage of attached old mortar in recycled coarse aggregate; k M , β: coefficient and index reflecting the influence of attached old mortar on elastic modulus; Three-dimensional porous graphene impact terms: G: Amount of three-dimensional porous graphene (kg / m 3 ); B: Total amount of cementitious materials (kg / m 3 ); The mass percentage of graphene in the cementitious material; k G , n: coefficient and index reflecting the graphene enhancement effect; 3D printing process parameters affecting: 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; k L , k S : Coefficient reflecting the influence of layer thickness and printing speed on elastic modulus; Step 2: Set the reference elastic modulus E c0 , actual density of concrete ρ c and the base density ρ0, the compressive strength f at age t c (t), baseline compressive strength f c0 The mass percentage of old mortar attached to the recycled coarse aggregate M and the replacement rate of recycled coarse aggregate Effective cementitious material dosage C eff and effective cementitious material dosage W eff ; 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 obtain a prediction model for the elastic modulus of recycled coarse aggregate 3D printed concrete.

2. The prediction model of elastic modulus of recycled coarse 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 / m3); 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 +R×M×W old W: mixing water consumption (kg / m3); W abs : Water absorption of recycled fine aggregate (%), expressed as mass percentage; W old : Water absorption rate of attached old mortar (%), expressed as mass percentage.

3. A prediction model for elastic modulus of recycled coarse 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%; set the coarse aggregate replacement rate 0%, 25%, 50%, 75%, 100%; Reference elastic modulus E c0 :25~35GPa; actual density of concrete ρ c : Determined based on the mass and volume of the test block; the mass percentage (%) of old mortar attached to the recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%.

4. A prediction model for elastic modulus of recycled coarse 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; 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 elastic modulus of recycled coarse aggregate 3D printed concrete according to claim 1, characterized in that: Material mixing: (1) Dry mixing: Mix the cementitious material, aggregate and graphene evenly; (2) Wet mixing: Add effective mixing water and admixtures to natural coarse aggregate and recycled coarse aggregate, stir evenly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

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