Prediction model for compressive strength of recycled fine aggregate 3D printing concrete

Abstract

The invention belongs to the field of civil engineering recycled fine aggregate 3D printing concrete compressive strength prediction, and relates to a model for predicting compressive strength of recycled fine aggregate 3D printing concrete. According to the method, a complex prediction formula is established through the effective mixing water dosage, the effective cementing material, the replacement rate of recycled fine aggregate, the content of attached old mortar, the content of three-dimensional porous graphene, the total amount of the cementing material, the dosage of carbon black, the thickness of a 3D printing layer, the thickness of a reference layer, the printing speed, the reference printing speed, the age and the reference age; the method has important significance for accurately predicting the compressive strength of the recycled fine material 3D printing concrete.

Description

Technical Field

[0001] The present invention relates to a strength prediction method for 3D printed concrete, and in particular to a prediction model for the compressive strength of recycled fine aggregate 3D printed concrete. Background Art

[0002] Given that recycled fine aggregates differ from natural fine aggregates in physical properties, such as high water absorption, high porosity, and high content of attached old mortar, 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 has been 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 broke through the bottleneck of insufficient strength of recycled fine aggregate 3D printed concrete, but also opened up a new path to solve this problem. This technological breakthrough is expected to accelerate the popularization of recycled fine aggregate 3D printed concrete in the commercial field and inject strong impetus into the booming development of its industry. From an economic perspective, the application of recycled fine aggregate 3D printing concrete will greatly promote the efficient use of resources and effective cost control; from an environmental perspective, it will help reduce the burden of construction waste on the ecological environment; from a social development perspective, the promotion and application of this technology will lead the construction industry towards a greener and more sustainable future. Therefore, the widespread application of this technology will undoubtedly have a far-reaching and immeasurable positive impact.

[0003] Recycled fine aggregate 3D printed concrete (RFA-3DPC) is a new type of building material that combines the resource utilization of construction waste with 3D printing technology. Due to the differences in the physical properties of 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. In order to improve the mechanical properties of recycled fine aggregate concrete, adding three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) and carbon black (Carbon Black, CB) nanomaterials can enhance the microstructure of concrete and improve its compressive strength. 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 compressive strength 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 compressive strength 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 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 compressive strength of 3D printed concrete containing recycled fine aggregate, as shown in formula (2):

[0007]

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

[0009] k is an empirical index related to the effective glue ratio, 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 The range of relevant empirical coefficients is: 0.2~0.6; k M The relevant empirical coefficient ranges from 0.23 to 0.26; k G The relevant empirical coefficient ranges from 13 to 16; k CB The relevant empirical coefficient ranges from 8 to 11; k L The range of relevant experience index is: 0.03~0.06; k S The range of relevant experience index is: 0.01~0.04;

[0010] C eff :Effective cementitious material dosage (kg / m 3 );

[0011] W eff :Effective mixing water consumption (kg / m 3 );

[0012] R: Recycled fine aggregate dosage (kg / m 3 );

[0013] A: Total amount of fine aggregate (kg / m 3 ), which is 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 (kg / m 3 );

[0016] CB: Carbon black dosage (kg / m 3 );

[0017] B: Total amount of cementitious materials (kg / m 3 );

[0018] t: age (days);

[0019] t 0 : Reference age, generally 28 days;

[0020] L t :3D printing layer thickness (mm);

[0021] L 0 : Reference layer thickness (mm), take the standard value, such as 10mm;

[0022] S: printing speed (mm / s);

[0023] S 0 : Reference printing speed (mm / s), take the standard value, such as 50mm / s.

[0024] Step 2: Set the effective cementitious material dosage C eff and effective mixing water volume W eff , recycled fine aggregate content R, total fine aggregate content A and 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 gelling material B, and the age t.

[0026] Step 4: Set the reference age t 0 , 3D printing layer thickness L t , reference layer thickness L 0 , Print speed S, Reference print speed S 0 .

[0027] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to predict the compressive strength of recycled fine aggregate 3D printed concrete.

[0028] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate 3D printed concrete, the effective cementitious material dosage C eff :

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

[0030] C: cement consumption (kg / m 3 );

[0031] F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 );

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

[0033] Effective mixing water consumption W eff :

[0034] W eff =W+R×W abs

[0035] W: Mixing water consumption (kg / m 3 );

[0036] W abs :Water absorption rate of recycled fine aggregate (%).

[0037] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate 3D printed 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, which is determined as a percentage of the total amount of cementitious materials, such as 0.1%, 0.3%, 0.5%, 1.0%; determine the amount of carbon black CB, which is determined as a percentage of the total amount of cementitious materials, such as 0, 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 fine aggregate M: 0, 10%, 15%, 20%, 25%.

[0038] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate 3D printed concrete, 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.

[0039] As a further solution of the present invention, a prediction model for the compressive strength 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 admixture are added, and the mixture is stirred evenly to ensure that the nanomaterials are fully dispersed.

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

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

[0042] (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

[0043] The following will be combined with the specific embodiments of the present invention to elaborate the technical solutions therein in detail and comprehensively. It should be noted that the embodiments described here are only some examples of the present invention, rather than exhaustive of all possible implementation methods. Based on these embodiments, all other embodiments that can be derived by those skilled in the art without the need for creative work should be deemed to fall within the scope of protection of the present invention.

[0044] 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 when the present invention is implemented. 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.

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

[0046] Table 1

[0047]

[0048] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0049] By public notice:

[0050]

[0051] The calculated predicted value (in MPa) is then compared with the measured value (in MPa) at 28 days. The comparison is shown in Table 2.

[0052] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 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

[0053] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 0.5MPa when predicting concrete strength, with the error between the actual value and the actual value being controlled within 0.5MPa, of which the minimum error is only 0.2MPa, the maximum error is 0.5MPa, and the average error reaches 0.44MPa. It is worth mentioning that the average relative error is only 0.94%, and the ratio of the predicted value to the actual value (Y predicted / Y measured) is stable in the range of 0.983 to 0.994, 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 compressive strength of concrete, but also has excellent prediction accuracy and precision.

[0054] 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 compressive strength of recycled fine aggregate 3D printed concrete, characterized in that The following steps are involved: Step 1: Establish a prediction 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 (MPa); k is related to the empirical coefficient, and its range is: 13 ~ 26; α is related to the empirical index of the effective glue ratio, and its range is: 0.8~0.9; 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 The range of relevant empirical coefficients is: 0.2~0.6; k M The relevant empirical coefficient ranges from 0.23 to 0.26; k G The relevant empirical coefficient ranges from 13 to 16; k CB The relevant empirical coefficient ranges from 8 to 11; k L The range of relevant experience index is: 0.03~0.06; k S The range of relevant experience index is: 0.01~0.04; C eff : Effective cementitious material dosage (kg / m3); W eff :Effective mixing water consumption (kg / m3); 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; 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 effective cementitious material dosage C eff and effective mixing water volume 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 predict the compressive strength of recycled fine aggregate 3D printed concrete.

2. A prediction model for compressive strength 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 (%).

3. A prediction model for compressive strength 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: as a percentage of the total amount of cementitious materials, such as 0.1%, 0.3%, 0.5%, 1.0%; determine the amount of carbon black CB, as a percentage of the total amount of cementitious materials, such as 0, 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 compressive strength of recycled fine aggregate 3D printed concrete according to claim 1, characterized in that: In step 1, three-dimensional porous graphene (G): high quality, good dispersion, 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 compressive strength 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 nanomaterials are fully dispersed.

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