Prediction model for compressive strength of 3D printing mortar

Abstract

The invention belongs to the field of civil engineering 3D printing mortar compressive strength prediction, and relates to a 3D printing mortar compressive strength prediction model. A complex prediction formula is established through the glue ratio, the effective cementing material dosage, the mortar-bone ratio, the three-dimensional porous graphene content and 3D printing process parameters, and the method has important significance on accurate prediction of the compressive strength of the 3D printing mortar.

Description

Technical Field

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

[0002] With the development of additive manufacturing technology, 3D printed mortar, as a new type of building material, combines traditional cement-based materials with advanced 3D printing technology, and has the advantages of complex component molding, high efficiency, and material saving. The introduction of three-dimensional porous graphene (3D-PG) can further enhance the mechanical properties and functional characteristics of mortar. However, due to the influence of factors such as interlayer bonding and printing parameters during the 3D printing process, it is somewhat complicated to predict the compressive strength of 3D printed mortar at 28 days of age under different 3D-PG contents. Summary of the invention

[0003] 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 3D printed mortar that simplifies the operating process and shortens the time consumption while ensuring that the error between the predicted results and the actual measured values ​​is small and the accuracy is high.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] Step 1: Establish a prediction model for the compressive strength of mortar containing 3D printing, as shown in formula (2):

[0006]

[0007] f C28 : Compressive strength of 3D printed mortar at 28 days of age (MPa);

[0008] k is an empirical coefficient ranging from 350 to 420; α is an empirical index related to the effective glue ratio ranging from 0.6 to 0.9; β is an empirical index related to the pulp-bone ratio ranging from 0.2 to 0.6; n is an empirical index related to the mass percentage of graphene in the cementitious material ranging from 0.5 to 0.8; k G The relevant empirical coefficient ranges from 12 to 22; k L Index related to printing thickness, ranging from 0.01 to 0.05; k S An empirical index related to printing speed, ranging from 0.04 to 0.07;

[0009] C eff :Effective cementitious material dosage (kg / m 3 );

[0010] W: effective mixing water consumption (kg / m 3 );

[0011] The volume ratio of paste to aggregate (paste-to-aggregate ratio);

[0012] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0013] B: Total amount of cementitious materials (kg / m 3 );

[0014] L t :3D printing layer thickness (mm);

[0015] S: Printing speed (mm / s).

[0016] Step 2: Set the effective cementitious material dosage C eff , effective mixing water consumption W eff , scleroderma-bone ratio

[0017] Step 3: Set the amount of three-dimensional porous graphene G and the total amount of gelling material B.

[0018] Step 4: Set the 3D printing layer thickness L t , printing speed S.

[0019] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to predict the compressive strength of the 3D printed mortar.

[0020] As a further solution of the present invention, the prediction model of the compressive strength of the 3D printing mortar, the effective cementitious material dosage C eff :

[0021] C eff =C+k F F+k SF SF+k SL SL

[0022] C: cement consumption (kg / m 3 );

[0023] F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 );

[0024] k F , k SF , k SL :Activity coefficient of each mineral admixture.

[0025] As a further solution of the present invention, the prediction model of the compressive strength of the 3D printing mortar has an effective water-binder ratio range of 0.3 to 0.45 and a mixing water amount 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%; the amount G of three-dimensional porous graphene is determined as a percentage of the total amount of the gelling material, such as 0.1%, 0.3%, 0.5%, and 1.0%.

[0026] As a further solution of the present invention, the prediction model of the compressive strength of the 3D printed mortar, three-dimensional porous graphene (G): high-quality, well-dispersed graphene material; the layer thickness is generally L t : 5~15mm, printing speed S: generally 3~100mm / s, pulp-bone ratio Value range: 0.35~0.45.

[0027] As a further solution of the present invention, a prediction model for the compressive strength of a 3D printed mortar is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material, the three-dimensional porous graphene and the sand are mixed evenly; and (2) wet mixing: the cementitious material, the three-dimensional porous graphene and the sand are mixed evenly.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) The present invention utilizes the water-binder ratio, the amount of effective cementitious material, the mortar-bone ratio, the three-dimensional porous graphene content and the 3D printing process parameters to quickly predict the compressive strength of the 3D printed mortar.

[0030] (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

[0031] 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.

[0032] In this specific implementation, the raw materials for producing the 3D printing mortar include cement, fly ash, slag, silica fume, three-dimensional porous graphene, and natural fine aggregate. In addition, the 3D printing mortar in the specific implementation of the present invention is not limited to the above raw materials. The cement in the above raw materials is ordinary silicate cement, and the fine aggregate is medium sand, with a grade matching standard.

[0033] Five groups of 3D printing mortar tests and verifications were carried out, and the mortar raw material ratios are shown in Table 1.

[0034] Table 1

[0035]

[0036] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0037] By formula:

[0038]

[0039] The calculated predicted value (in MPa) is then compared with the measured value (in MPa) at 28 days, as shown in Table 2:

[0040] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 40.3 40.1 0.2 0.50 0.995 2 53.4 52.8 0.6 1.12 0.989 3 41.4 40.9 0.5 1.21 0.988 4 47.3 46.5 0.8 1.69 0.983 5 36.7 35.7 1 2.72 0.973

[0041] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 1MPa of 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 0.62MPa. It is worth mentioning that the average relative error is only 1.45%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 0.973 to 0.995, showing an accuracy rate of more than 98%. These data fully prove that the prediction formula in the present invention is not only highly correlated when calculating the 28d compressive strength of mortar, but also has excellent prediction precision and accuracy.

[0042] 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 3D printed mortar, characterized in that The following steps are involved: Step 1: Establish a prediction model for the compressive strength of mortar containing 3D printing, as shown in formula (1): f C28 : Compressive strength of 3D printed mortar at 28 days of age (MPa); k is the empirical coefficient, which ranges from 350 to 420; α is the empirical index related to the effective glue ratio, which ranges from: 0.6~0.9; β is an empirical index related to the pulp-bone ratio, and its range is: 0.2~0.6; n is an empirical index related to the mass percentage of graphene in the cementitious material, and its range is: 0.5~0.8; k G The range of relevant empirical coefficients is: 12~22; k L Index related to printing thickness, ranging from 0.01 to 0.05; k S An empirical index related to printing speed, Its range is: 0.04~0.07; C eff :Effective cementitious material dosage (kg / m 3 ); W: Mixing water consumption (kg / m 3 ); The volume ratio of paste to aggregate (paste-to-aggregate ratio); G: Amount of three-dimensional porous graphene (kg / m 3 ); B: Total amount of cementitious materials (kg / m 3 ); L t :3D printing layer thickness (mm); S: printing speed (mm / s); Step 2: Set the effective cementitious material dosage C eff and effective mixing water volume W eff , scleroderma-bone ratio Step 3, setting the amount of three-dimensional porous graphene G and the total amount of gelling material B; Step 4: Set the 3D printing layer thickness L t , printing speed S; Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to predict the compressive strength of the 3D printed mortar.

2. A prediction model for compressive strength of 3D printed mortar 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.

3. A prediction model for compressive strength of 3D printed mortar 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 amount 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 as a percentage of the total amount of gelling material, such as 0.1%, 0.3%, 0.5%, 1.0%.

4. A prediction model for compressive strength of 3D printed mortar according to claim 1, characterized in that: In step 1, three-dimensional porous graphene (G): high-quality, well-dispersed graphene; layer thickness L t : Generally 5-15mm, printing speed S: Generally 30-100mm / s, pulp-bone ratio Value range: 0.35~0.

45.

5. A prediction model for compressive strength of 3D printed mortar according to claim 1, characterized in that: Material mixing: (1) Dry mixing: the cementitious material, the three-dimensional porous graphene and the sand are mixed evenly; (2) Wet mixing: the cementitious material, the three-dimensional porous graphene and the sand are mixed evenly.

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