CN115019909B - Method for predicting compression strength of alkali-activated slag concrete - Google Patents
Method for predicting compression strength of alkali-activated slag concrete Download PDFInfo
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- 239000002893 slag Substances 0.000 title claims abstract description 40
- 239000003513 alkali Substances 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000006835 compression Effects 0.000 title abstract description 9
- 238000007906 compression Methods 0.000 title abstract description 9
- NTHWMYGWWRZVTN-UHFFFAOYSA-N sodium silicate Chemical compound [Na+].[Na+].[O-][Si]([O-])=O NTHWMYGWWRZVTN-UHFFFAOYSA-N 0.000 claims abstract description 44
- 235000019353 potassium silicate Nutrition 0.000 claims abstract description 30
- 239000004115 Sodium Silicate Substances 0.000 claims abstract description 14
- 229910052911 sodium silicate Inorganic materials 0.000 claims abstract description 14
- 239000000463 material Substances 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims description 21
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 8
- 229910004298 SiO 2 Inorganic materials 0.000 claims description 4
- 229910052681 coesite Inorganic materials 0.000 claims description 4
- 229910052906 cristobalite Inorganic materials 0.000 claims description 4
- 239000000377 silicon dioxide Substances 0.000 claims description 4
- 239000011734 sodium Substances 0.000 claims description 4
- 229910052682 stishovite Inorganic materials 0.000 claims description 4
- 229910052905 tridymite Inorganic materials 0.000 claims description 4
- 239000007787 solid Substances 0.000 claims description 3
- 229910004742 Na2 O Inorganic materials 0.000 claims description 2
- KKCBUQHMOMHUOY-UHFFFAOYSA-N Na2O Inorganic materials [O-2].[Na+].[Na+] KKCBUQHMOMHUOY-UHFFFAOYSA-N 0.000 claims description 2
- 238000006386 neutralization reaction Methods 0.000 claims description 2
- 238000004448 titration Methods 0.000 claims description 2
- 239000007788 liquid Substances 0.000 claims 2
- 238000001514 detection method Methods 0.000 claims 1
- 229910052708 sodium Inorganic materials 0.000 claims 1
- 238000012360 testing method Methods 0.000 abstract description 10
- 239000004566 building material Substances 0.000 abstract description 2
- 238000012423 maintenance Methods 0.000 abstract description 2
- 239000004568 cement Substances 0.000 abstract 1
- 238000010276 construction Methods 0.000 abstract 1
- 238000003908 quality control method Methods 0.000 abstract 1
- 230000007774 longterm Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/50—Reuse, recycling or recovery technologies
- Y02W30/91—Use of waste materials as fillers for mortars or concrete
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- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Curing Cements, Concrete, And Artificial Stone (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The invention discloses a method for predicting compression strength of alkali-activated slag concrete, and belongs to the field of new building materials. The method is suitable for a system in which slag is excited by water glass. By measuring the concrete material parameters: sodium silicate modulus (M s), sodium silicate concentrationThe water-cement ratio (w/b) realizes the rapid prediction of the compressive strength of the alkali-activated slag concrete for 28 days and 90 days, and a test piece is not required to be molded and is tested after long-time maintenance, thus providing a new method for product quality management and construction quality control.
Description
Technical Field
The invention belongs to the technical field of building materials, and particularly relates to a method for predicting the compressive strength of alkali-activated slag concrete.
Background
Compressive strength is an important technical index of alkali-activated slag concrete, and directly influences engineering application of the alkali-activated slag concrete.
At present, the compression strength test of alkali-activated slag concrete is carried out according to GB/T50081-2019 of the test method Standard of physical and mechanical properties of concrete. The method comprises the following specific steps: and (3) molding a concrete test piece according to the material proportion, curing the concrete test piece to a specified age in a standard curing room with the temperature (20+/-2) ℃ and the humidity of more than 95%, performing a pressure test, and calculating the compressive strength according to a formula 11.
F cu =f/a equation 11
Wherein, f cu -the compressive strength (unit: MPa) of the concrete test piece, and the calculation result should be accurate to 0.1MPa;
F-concrete breaking load (N);
a-concrete bearing area (mm 2).
The method needs to prepare a test piece and carry out maintenance for a long time, and influences the engineering progress to a certain extent.
Disclosure of Invention
The invention provides a method for predicting 28-day and 90-day compressive strength of alkali-activated slag concrete, which does not need to prepare a test piece and needs a long curing period.
In order to achieve the above object, the present invention adopts the following technical scheme:
A method for predicting the compressive strength of alkali-activated slag concrete is characterized by comprising the following steps: the method comprises the following steps:
detecting and calculating alkali-activated slag concrete material parameters: water glass modulus, water glass alkali concentration and water-gel ratio;
three response surface functions were calculated according to the following equations 1-3 And/>Is a value of (2);
Then calculating the compressive strength f cu of the alkali-activated slag concrete according to the following formula 4-formula 5;
f cu＝0.95*(D1X3+D2X2+D3X+D4) equation 5
Wherein M S is the modulus of water glass,The concentration of sodium silicate is water glass alkali, and w/b is water-gel ratio;
A 1-A5、B1-B5、C1-C6、D1-D4 is the fitting coefficient.
When the compressive strength f cu,28 of the 28-day alkali-activated slag concrete is calculated,
A 1 is 31651.11, A 2 is-10177.44, A 3 is 928.48, A 4 is-15.23, and A 5 is-0.67; b 1 is 0, B 2 is-0.95, B 3 is 2.85, B 4 is-1.81, and B 5 is-0.10; c 1 is-64276.12, C 2 is 159711.65, C 3 is 157664.36, C 4 is 77025.02, C 5 is 18655.13, and C 6 is 1793.01; d 1 is-0.17, D 2 is-1.29, D 3 is 27.23, and D 4 is 61.18.
When the compressive strength f cu,28 of the 90-day alkali-activated slag concrete is calculated,
A 1 is 0, A 2 is 0, A 3 is-0.42, A 4 is 1.73, and A 5 is-1.12; b 1 is-7478254.23, B 2 is 1556660.92, B 3 is 116333.81, B 4 is 3704.76, and B 5 is-42.49; c 1 takes on a value of-6510.94, C 2 takes on a value of 14518.42, C 3 takes on a value of 12479.24, C 4 takes on a value of 5136.97, C 5 takes on a value of 1008.63, and C 6 takes on a value of 75.67; d 1 is 5.87, D 2 is 11.46, D 3 is 19.04, and D 4 is 55.34.
The fitting coefficient a 1-A5、B1-B5、C1-C6、D1-D4 was determined by non-parametric regression calculation.
The sodium silicate modulus (M S) is calculated according to the formula 6 by measuring Na 2 O% and SiO 2% in the sodium silicate by a neutralization titration method.
M S＝SiO2％/Na2 O% formula 6
Sodium silicate concentrationCalculated according to equation 7.
Where WG is the water glass usage and slag is the slag usage.
The water-gel ratio (w/b) is calculated according to the formula 8.
W/b= (water+wg x L WG)/(slag+WG*SWG) equation 8
Wherein water is water consumption, S WG is solid content S WG＝Na2O％+SiO2％,LWG in water glass and L WG＝100％-SWG in water glass.
The calculation results of equations 1-4 are accurate to 0.000001 and the calculation result of equation 5 is accurate to 0.01.
The calculation results of the formula 6 and the formula 8 are accurate to 0.01, and the calculation result of the formula 7 is accurate to 1%.
The prediction method provided by the invention uses two evaluation difference indexes of RMSE and MAE. The calculation method is shown in the formula 9 and the formula 10. The closer the RMSE and MAE are to 0, the smaller the error and the higher the model accuracy.
Wherein y i(y1,y2……yn) is the measured value of f cu,For the f cu predictor, n is the number of data samples.
The beneficial effects are that:
The method for predicting the compressive strength of the alkali-activated slag concrete provided by the invention provides a relation model among the water glass modulus, the water glass alkali concentration, the water gel ratio and the compressive strength, and can rapidly predict the compressive strength of the alkali-activated slag concrete. The test piece does not need to be prepared and maintained for a long time, and the calculation process is simple and quick.
Drawings
Figure 1 day compression strength: water glass modulus M S and response surface functionIs a relationship of (2);
figure 2 day compression strength: sodium silicate concentration And response surface function/>Is a relationship of (2);
figure 3 day compression strength: water-gel ratio w/b and response surface function Is a relationship of (2);
Figure 4 day compression strength: relationship between X and long term compressive strength f cu,28;
figure 5 90 day compressive strength: water glass modulus M S and response surface function Is a relationship of (2);
figure 6 90 day compressive strength: sodium silicate concentration And response surface function/>Is a relationship of (2);
figure 7 90 day compressive strength: water-gel ratio w/b and response surface function Is a relationship of (2);
figure 8 90 day compressive strength: relationship between X and long term compressive strength f cu,90;
Figure 9 day compression strength: relation between measured value and predicted value;
figure 10 90 day compressive strength: relation between measured and predicted values.
Detailed Description
The compressive strength of alkali-activated slag concrete is related to material factors and environmental factors, including water glass modulus M S, water glass alkali concentrationWater-gel ratio w/b, slag specific surface area, slag basicity coefficient, etc. The research finds that: the most important influencing factor of alkali-activated slag concrete is water glass modulus M S, water glass alkali concentration/>And the water-gel ratio w/b. In comparison experiments, such as taking into account the water glass modulus M S, the water glass alkali concentration/>When the specific surface area of slag and the slag alkalinity coefficient are considered at the same time of the water-gel ratio w/b, the correlation index (closer to 1, the better the fitting goodness is indicated) only rises by 0.05, which shows that the two latter factors have little influence on the accuracy of the prediction result. On the other hand, consider the response surface function/>, of each of the latter two factorsIs quite discrete. For non-parametric regression techniques, the very discrete response surface function proves to have little effect on the results. Therefore, the prediction method of the invention finally establishes the compressive strength of alkali-activated slag concrete, the modulus M S of water glass and the alkali concentration/>, of the water glassThe relation model of the water-gel ratio w/b is realized, namely the formulas 1-5.
Fitting coefficients a 1-A5、B1-B5、C1-C6、D1-D4 in the relational model are determined by non-parametric regression calculations. The non-parametric regression calculation process includes:
A large amount of data on compressive strength and material parameters of actual alkali-activated slag concrete materials described in the published literature are collected as sample data for non-parametric regression calculation. In the present invention, 309 sets of samples were collected for 28-day compressive strength; 145 sets of samples relating to 90-day compressive strength, including sodium silicate modulus M S, sodium silicate concentration The water to gel ratio w/b, the 28 day compressive strength f cu,28, and the 90 day compressive strength f cu,90 data.
Sample data is imported into software S-PLUS to carry out non-parameter regression calculation to obtain corresponding sodium silicate modulus M S and sodium silicate alkali concentrationFitting coefficients of the water-gel ratio w/b are fitted to form three response surface functions, wherein the corresponding fitting relationships are shown in fig. 1-4 (about f cu,28) and fig. 5-8 (about f cu,90) respectively, according to the formulas 1-3. Further performing non-parametric regression calculation with the sum of the response surface function values of formulas 1-3 to obtain fitting coefficients with f cu,28 and f cu,90, so as to obtain functions of f cu,28 and f cu,90 by fitting, see formulas 4-5.
Since the present invention has collected data samples relating to 28-day compressive strength and 90-day compressive strength, specific values of the fitting coefficients in the two-cycle examples were obtained by fitting. It will be appreciated that equations 1-5 are equally applicable to other cycle embodiments, and that only non-parametric regression calculations with actual compressive strength and material parameter data for the corresponding cycle may be required to fit and derive the compressive strength response surface function for the corresponding cycle.
The method is described in detail below by way of specific examples to verify the rationality and accuracy of the method.
Examples
By substituting 4 groups of alkali-activated slag concrete material parameters shown in Table 1 into formulas 1-3 and according to formula 4 and formula 5, the compressive strengths f cu,28 and f cu,90 of the concrete for 28 days and 90 days can be calculated, and the results are shown in Table 2.
TABLE 1 Material parameters
Concrete test pieces were prepared using the material parameters in table 1, and compressive strength was measured after standard curing to the required age, and the measured values of alkali-activated slag concrete strength were obtained according to formula 11, and the results are shown in table 2.
The predicted 28-day compressive strength and the measured compressive strength are compared with each other in FIG. 9, and the predicted 90-day compressive strength and the measured compressive strength are compared with each other in FIG. 10; the error after evaluating the difference according to formulas 9 and 10 is shown in table 3. The closer the model errors RMSE, MAE are to 0, the higher the model accuracy, the more accurate the prediction, and the more accurate the model errors are in the range of 0-20. As can be seen from the data in Table 3, the model errors RMSE and MAE are smaller than 2, the model accuracy is higher, the predicted values of the compressive strength of the alkali-activated slag concrete for 28 days and 90 days are basically consistent with the actual measured values, the error approaches 0, and the method is reasonable and accurate.
TABLE 2 predicted and measured intensity values
TABLE 3 error results
Claims (7)
1. A method for predicting the compressive strength of alkali-activated slag concrete is characterized by comprising the following steps: the method comprises the following steps:
detecting and calculating alkali-activated slag concrete material parameters: water glass modulus, water glass alkali concentration and water-gel ratio;
three response surface functions were calculated according to the following equations 1-3 And/>Is a value of (2);
Then calculating the compressive strength f cu of the alkali-activated slag concrete according to the following formula 4-formula 5;
f cu＝0.95*(D1X3+D2X2+D3X+D4) equation 5
Wherein M S is the modulus of water glass,The concentration of sodium silicate is water glass alkali, and w/b is water-gel ratio;
a 1-A5、B1-B5、C1-C6、D1-D4 is the fitting coefficient.
2. The method according to claim 1, characterized in that:
when the compressive strength f cu,28 of the 28-day alkali-activated slag concrete is calculated,
A 1 is 31651.11, A 2 is-10177.44, A 3 is 928.48, A 4 is-15.23, and A 5 is-0.67; b 1 is 0, B 2 is-0.95, B 3 is 2.85, B 4 is-1.81, and B 5 is-0.10; c 1 is-64276.12, C 2 is 159711.65, C 3 is 157664.36, C 4 is 77025.02, C 5 is 18655.13, and C 6 is 1793.01; d 1 is-0.17, D 2 is-1.29, D 3 is 27.23, and D 4 is 61.18.
3. The method according to claim 1, characterized in that:
When the compressive strength f cu,28 of the 90-day alkali-activated slag concrete is calculated,
A 1 is 0, A 2 is 0, A 3 is-0.42, A 4 is 1.73, and A 5 is-1.12; b 1 is-7478254.23, B 2 is 1556660.92, B 3 is 116333.81, B 4 is 3704.76, and B 5 is-42.49; c 1 takes on a value of-6510.94, C 2 takes on a value of 14518.42, C 3 takes on a value of 12479.24, C 4 takes on a value of 5136.97, C 5 takes on a value of 1008.63, and C 6 takes on a value of 75.67; d 1 is 5.87, D 2 is 11.46, D 3 is 19.04, and D 4 is 55.34.
4. The method according to claim 1, characterized in that: the fitting coefficient a 1-A5、B1-B5、C1-C6、D1-D4 was determined by non-parametric regression calculation.
5. The method according to claim 1, characterized in that:
Detecting and calculating the water glass modulus includes: the concentration of Na 2 O and SiO 2 in the water glass was determined by neutralization titration and calculated according to equation 6:
M S＝SiO2％/Na2 O% formula 6
Wherein SiO 2% and Na 2 O% are the concentrations of SiO 2 and Na 2 O, respectively;
The detection and calculation of sodium silicate concentration comprises: the water glass dosage and the slag dosage are weighed and calculated according to the formula 7:
Wherein WG is the water glass dosage and slag is the slag dosage;
Detecting and calculating the water-gel ratio comprises: the water consumption is weighed, the solid content and the liquid content in the water glass are calculated, and the calculation is carried out according to the formula 8:
w/b= (water+wg x L WG)/(slag+WG*SWG) equation 8
Wherein water is water consumption, S WG is solid content in water glass, S WG＝Na2O％+SiO2％,LWG is liquid content in water glass, and L WG＝100％-SWG.
6. The method according to claim 1, characterized in that: the calculation results of equations 1-4 are accurate to 0.000001, and the calculation result of equation 5 is accurate to 0.01.
7. The method according to claim 5, wherein: the calculation results of equation 6 and equation 8 are accurate to 0.01,
The calculation of equation 7 is accurate to 1%.
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