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

Abstract

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

[0002] As the construction industry continues to improve its standards for environmental protection and sustainable resource utilization, recycled coarse aggregate (RCA) is increasingly used in the field of concrete as an economical and environmentally friendly alternative material. However, the old mortar attached to the surface of recycled aggregate may have an adverse effect on the mechanical properties of concrete, especially the compressive strength. In the field of 3D printed concrete, the printing quality depends not only on the material ratio, but also on 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 printed concrete has become a key issue that needs to be solved urgently.

[0003] Most of the current research focuses on the impact of a single factor on concrete performance, while there is still a lack of a comprehensive model that considers the effects of multiple factors for the prediction of the compressive strength of recycled coarse aggregate 3D printed concrete. In view of this, it is of great significance to construct a prediction model for the compressive strength of recycled coarse aggregate 3D printed concrete that covers multiple factors such as effective water-cement ratio, recycled coarse aggregate replacement rate, old mortar adhesion, three-dimensional porous graphene addition, age, and 3D printing process parameters for scientifically predicting the compressive strength of concrete and guiding its application in engineering practice. 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 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 compressive strength of 3D printed concrete containing recycled coarse 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 14 to 19; α is an empirical index related to the effective glue ratio, ranging from 0.8 to 1.0; β is an empirical index related to the replacement rate of recycled coarse aggregate, ranging from 0.3 to 0.6; γ is an empirical index related to the age ratio, ranging from 0.20 to 0.30; δ 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; 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; k G The relevant empirical coefficient ranges from 17 to 19; 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] W eff :Effective mixing water consumption (kg / m 3 ), taking into account the water absorption of recycled coarse aggregate and the water absorption of the attached old mortar;

[0011] R: Recycled fine aggregate dosage (kg / m 3 );

[0012] G t :Total amount of coarse aggregate (kg / m 3 ), which is the sum of natural coarse aggregate and recycled coarse aggregate;

[0013] M: mass percentage of old mortar attached to recycled coarse aggregate (%);

[0014] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0015] B: Total amount of cementitious materials (kg / m 3 );

[0016] t: age (days);

[0017] t0: reference age, generally 28 days;

[0018] L t :3D printing layer thickness (mm);

[0019] L0: reference layer thickness (mm), take the standard value, such as 10mm;

[0020] S: printing speed (mm / s);

[0021] S0: Reference printing speed (mm / s), take the standard value, such as 50mm / s.

[0022] Step 2: Set the effective cementitious material dosage C eff , effective mixing water consumption W eff , Recycled coarse aggregate replacement rate The mass percentage M of old mortar attached to recycled coarse aggregate.

[0023] 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.

[0024] Step 4: Set the 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0.

[0025] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to predict the compressive strength of recycled coarse aggregate 3D printed concrete.

[0026] As a further solution of the present invention, the prediction model of the compressive strength of the recycled coarse aggregate 3D printed concrete, the effective cementitious material dosage C eff :

[0027] C eff =C+k F F+k SF SF+k SL SL

[0028] C: cement consumption (kg / m 3 );

[0029] F, SF, SL: the amount of fly ash, silica ash, and slag powder (kg / m 3 );

[0030] k F , k SF , k SL : Activity coefficient of each mineral admixture;

[0031] Effective mixing water consumption W eff :

[0032] W eff =W+R×W abs +R×M×W old

[0033] W: Mixing water consumption (kg / m 3 )

[0034] W abs : Water absorption rate of recycled coarse aggregate (%), expressed as mass percentage;

[0035] W old: Water absorption rate of attached old mortar (%), expressed as mass percentage.

[0036] As a further solution of the present invention, the prediction model of the compressive strength of the recycled coarse 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 ; 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 mass percentage (%) of the old mortar attached to the recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%; the replacement rate of recycled coarse aggregate Such as 0, 25%, 50%, 75%, 100%.

[0037] As a further solution of the present invention, the prediction model of the compressive strength of the recycled coarse 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.

[0038] As a further solution of the present invention, a prediction model for the compressive strength of recycled coarse aggregate 3D printed concrete is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material (including graphene), natural coarse aggregate, recycled coarse aggregate and natural fine aggregate are mixed evenly; and (2) wet mixing: effective mixing water and admixtures are added and stirred evenly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] (1) The present invention utilizes the three-dimensional porous graphene content, recycled coarse aggregate content, attached old mortar content, age and 3D printing process parameters to quickly predict the compressive strength of recycled coarse aggregate 3D printed concrete.

[0041] (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

[0042] 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.

[0043] 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.

[0044] 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:

[0045] Table 1

[0046]

[0047] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0048] By formula:

[0049]

[0050] 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:

[0051] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 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

[0052] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is controlled within 1MPa from the measured value when predicting concrete strength, with the minimum error being only 0.2MPa, the maximum error being 1MPa, and the average error reaching 0.54MPa. It is worth mentioning 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 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.

[0053] 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 coarse aggregate 3D printed concrete, characterized in that The following steps are included Step 1: Establish a prediction model for the compressive strength of 3D printed concrete containing recycled coarse 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: 14 ~ 19; α is related to the empirical index of the effective glue ratio, and its range is: 0.8~1.0; β is an empirical index related to the replacement rate of recycled coarse aggregate, and its range is: 0.3~0.6; γ is an empirical index related to the age ratio, and its range is: 0.20~0.30; δ is an empirical index related to the content of attached old mortar, Its range is: 0.2~0.4; n is an empirical index related to the mass percentage of graphene in the gelling material, and its range is: 0.5~0.7; k R The range of relevant empirical coefficients is: 0.30~0.40; k M The range of relevant empirical coefficients is: 0.24~0.26; k G The relevant empirical coefficient ranges from 17 to 19; 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), taking into account the water absorption rate of recycled coarse aggregate and the water absorption of attached old mortar; R: amount of recycled fine aggregate (kg / m3); G t : Total amount of coarse aggregate (kg / m3), i.e. the sum of natural coarse aggregate and recycled coarse aggregate; M: mass percentage of old mortar attached to recycled coarse aggregate (%); G: three-dimensional porous graphene 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 coarse aggregate replacement rate The mass percentage of old mortar attached to the recycled coarse aggregate M; 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 predict the compressive strength of recycled coarse aggregate 3D printed concrete.

2. A prediction model for compressive strength 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 / 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 +R×M×W old W: Mixing water consumption (kg / m 3 ) W abs : Water absorption rate of recycled coarse aggregate (%), expressed as mass percentage; W old : Water absorption rate of attached old mortar (%), expressed as mass percentage.

3. A prediction model for compressive strength 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, and 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 mass percentage (%) of old mortar attached to recycled coarse aggregate M: 0, 10%, 15%, 20%, 25%; the replacement rate of recycled coarse aggregate Such as 0, 25%, 50%, 75%, 100%.

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

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