CN116663378A - Grading optimization method considering morphology of reclaimed sand particles - Google Patents
Grading optimization method considering morphology of reclaimed sand particles Download PDFInfo
- Publication number
- CN116663378A CN116663378A CN202310602978.4A CN202310602978A CN116663378A CN 116663378 A CN116663378 A CN 116663378A CN 202310602978 A CN202310602978 A CN 202310602978A CN 116663378 A CN116663378 A CN 116663378A
- Authority
- CN
- China
- Prior art keywords
- aggregate
- grading
- morphology
- reclaimed sand
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000002245 particle Substances 0.000 title claims abstract description 63
- 239000004576 sand Substances 0.000 title claims abstract description 53
- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 29
- 239000011800 void material Substances 0.000 claims abstract description 44
- 238000009826 distribution Methods 0.000 claims abstract description 43
- 238000012795 verification Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 238000012360 testing method Methods 0.000 claims description 14
- 238000013461 design Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000001186 cumulative effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 239000002994 raw material Substances 0.000 claims description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 43
- 239000004570 mortar (masonry) Substances 0.000 description 19
- 230000000694 effects Effects 0.000 description 7
- 238000012856 packing Methods 0.000 description 7
- 238000009825 accumulation Methods 0.000 description 4
- 239000002002 slurry Substances 0.000 description 4
- 101100524528 Escherichia coli (strain K12) rfaC gene Proteins 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000004566 building material Substances 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- CJBPIZBHRWDBGQ-COSFPPCYSA-N rfa-1 Chemical compound C1([C@H]2N[C@H](CC3(N=C4C=5C6=C7O[C@](C6=O)(C)O/C=C/[C@@H]([C@H]([C@@H](OC(C)=O)[C@@H](C)[C@@H](O)[C@H](C)[C@@H](O)[C@@H](C)\C=C\C=C(C)/C(=O)NC(=C4N3)C(=O)C=5C(O)=C7C)C)OC)C2)C=2C=CC=CC=2)=CC=CC=C1 CJBPIZBHRWDBGQ-COSFPPCYSA-N 0.000 description 2
- BINCGEGLXIMJFO-JQSLHUNDSA-N rfa-2 Chemical compound C1([C@H]2N[C@H](CC3(N=C4C=5C6=C7O[C@](C6=O)(C)O/C=C/[C@@H]([C@H]([C@@H](OC(C)=O)[C@@H](C)[C@@H](O)[C@H](C)[C@@H](O)[C@@H](C)\C=C\C=C(C)/C(=O)NC(=C4N3)C(=O)C=5C(O)=C7C)C)OC)C2)C=2C=CC(F)=CC=2)=CC=C(F)C=C1 BINCGEGLXIMJFO-JQSLHUNDSA-N 0.000 description 2
- 239000006004 Quartz sand Substances 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 239000004567 concrete Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a grading optimization method considering the morphology of reclaimed sand particles, which belongs to the technical field of particle grading optimization and comprises the following steps: s1: establishing a three-dimensional ellipsoidal aggregate random throwing model; s2: correcting the MAA model based on the morphology parameters of the reclaimed sand particles; s3: and carrying out verification comparison analysis on the corrected model. According to the invention, the characteristic of the morphology of the reclaimed sand particles is considered, the feature factor of the length-diameter ratio is introduced for the first time to correct the MAA model, the relation between the distribution coefficient and the length-diameter ratio is established, and the relation between the distribution coefficient and the length-diameter ratio and the void ratio in the existing MAA model is established by considering the morphology characteristics of the particles.
Description
Technical Field
The invention belongs to the technical field of particle grading optimization, and particularly relates to a grading optimization method considering the morphology of reclaimed sand particles.
Background
The reclaimed sand is a green building material which can replace natural sand. Compared with natural sand, the reclaimed sand has complex sources and large particle morphology difference. The grading optimization of the reclaimed sand is beneficial to improving the strength and the performance of the reclaimed mortar and the reclaimed concrete. The basic assumption conditions of the existing regenerated sand grain composition optimization model are as follows: all aggregates are ideal spherical aggregates. This is not in line with practice, so that it is difficult to obtain good grain composition by adopting the traditional grading optimization method in the practical application process, and the engineering application of the reclaimed sand is seriously affected.
Disclosure of Invention
Accordingly, the present invention aims to provide a grading optimization method considering the morphology of reclaimed sand particles
In order to achieve the above purpose, the present invention provides the following technical solutions:
a grading optimization method considering the morphology of reclaimed sand particles comprises the following steps:
s1: establishing a three-dimensional ellipsoidal aggregate random throwing model;
s2: correcting the MAA model based on the morphology parameters of the reclaimed sand particles;
s3: and carrying out verification comparison analysis on the corrected model.
Further, the step S1 specifically includes the following steps:
s11: defining a space boundary and size range and grading distribution of aggregate;
s12: aggregate is put in a fixed space range, aggregate grading is put in according to the size from large to small, when the aggregate put volume in the size range is larger than the aggregate volume under the target grading, the aggregate put in the current grading range is ended, and the aggregate put in the next size range is carried out;
s13: randomly generating the centroid position of each aggregate, simultaneously randomly generating the triaxial size, the rotation matrix and the displacement matrix of the aggregate, and ensuring that the aggregate is in the boundary; the triaxial size of the aggregate needs to meet the particle size range of the aggregate;
s14: judging whether the aggregates accord with boundary conditions or not and judging the position relationship among the aggregates;
s15: and repeating the steps S11-S14 until the aggregate is successfully put in all the size ranges, and storing the geometric characteristics and the spatial position information of all the aggregates.
Further, the step S2 specifically includes the following steps:
s21: determining the particle size of the ellipsoidal aggregate put in the test, the distribution coefficient n in the MAA model and the range of the length-diameter ratio of the actual aggregate;
s22: utilizing the established three-dimensional ellipsoidal aggregate random throwing model to throw aggregates with different distribution coefficients and length-diameter ratios;
s23: generating a random throwing result graph of ellipsoidal fine aggregate and storing corresponding void ratio and particle size distribution results;
s24: and (3) taking the length-diameter ratio as a characteristic factor, and calculating the correlation between the length-diameter ratio AR and the system porosity by combining the test results in a fitting way, wherein the fitting result is obtained.
Further, in step S24, the distribution coefficient n of the aspect ratio AR and the void fraction v o The calculation formula of (2) is as follows:
n＝0.3225+0.05AR
v o ＝0.1924+0.007AR+0.014AR 2 。
further, the MAA model corrected by the aspect ratio is represented by the following formula:
wherein: CPFT (%) is the cumulative sieve allowance smaller than the particle diameter D; d (D) L Indicating the maximum particle size in the stacking system; d (D) S Represents the smallest particle size in the particle system; d represents the current particle size; AR is the size of the longest axis to the shortest axis in the three axes of the aggregate in the stacking system, and AR is more than or equal to 1.
Further, the step S3 specifically includes the following steps:
s31: determining parameters of the modified model and raw material attribute parameters;
s32: grading under different grading models is selected for proportion design;
s34: and verifying and comparing the void ratio, the working performance and the mechanical performance under different matching optimization schemes.
The invention has the beneficial effects that: the invention considers the feature of the appearance of the reclaimed sand particles and programs the three-dimensional ellipsoidal aggregate generation and space delivery algorithm; the feature factor of the length-diameter ratio is introduced for the first time to correct the MAA model, the relation between the distribution coefficient and the length-diameter ratio is established, and compared with the traditional grading optimization method, the application range is wider, and the grading design is more reasonable; the invention considers the morphological characteristics of particles and establishes the relationship between the distribution coefficient and the length-diameter ratio and the void ratio in the existing MAA model.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a flow chart of a grading optimization method considering the morphology of reclaimed sand particles;
FIG. 2 is a graph showing cumulative screen residue curves under different distribution coefficients n;
FIG. 3 is a graph showing the result of random placement of ellipsoidal fine aggregates with different aspect ratios with a distribution coefficient n of 0.37;
FIG. 4 is a graph of void fraction variation at different distribution coefficients n;
FIG. 5 is a formula fit of aspect ratio to distribution coefficient and void fraction;
FIG. 6 is a graph of dense packing void fraction vs;
FIG. 7 is a graph showing the effect of gradation on mortar fluidity;
FIG. 8 is a graph showing the effect of gradation on mortar compressive strength.
Detailed Description
As shown in fig. 1, the invention provides a reclaimed sand grain composition optimization method, which solves the problem that the existing grain composition optimization method cannot realize the optimal composition design in the process of performing composition optimization on reclaimed sand, and comprises the following specific contents:
step one, establishing a three-dimensional ellipsoidal aggregate random throwing model:
(1) Defining a space boundary, a size range of aggregate and grading distribution, wherein the aggregate putting space in the case is a cubic space of 100mm x 100mm, and the particle size distribution of the aggregate is 0.15mm-4.75mm;
(2) Aggregate is put in a fixed space range, aggregate grading is put in according to the size from large to small, when the aggregate put volume in the size range is larger than the aggregate volume under the target grading, the aggregate put in the current grading range is ended, and the aggregate put in the next size range is carried out;
(3) Randomly generating the centroid position of each aggregate, and simultaneously randomly generating the triaxial size (the triaxial size needs to meet the particle size range of the aggregate), the rotation matrix and the displacement matrix of the aggregate, and ensuring that the aggregate is in the boundary;
(4) The spatial placement effectiveness determination of ellipsoids can be divided into the following two aspects: and judging the positions of the ellipsoids and the space boundaries, namely judging whether the ellipsoids are in space or not. Whether the boundary crossing condition exists; and judging the position between the ellipsoids, namely judging whether the ellipsoids have superposition conditions or not, and whether the ellipsoids meet the space limitation requirement or not. To determine the effectiveness of ellipsoidal delivery, both factors need to be considered. If both aspects meet the requirements, the ellipsoid is put into effect, otherwise, the ellipsoid is not put into effect;
(5) Repeating the steps until the aggregate is successfully put in all the size ranges, and storing the geometric characteristics and the spatial position information of all the aggregates;
correcting the MAA model based on the morphology parameters of the reclaimed sand particles:
(1) Determining the particle size of the ellipsoidal aggregate put in the test, the distribution coefficient n in the MAA model and the range of the length-diameter ratio of the actual aggregate; the cumulative percentage of screen residue curve of the particle size range of the ellipsoidal aggregate put in the test under different distribution coefficients n is shown in figure 2. As known from the prior study of grading optimization by applying MAA model, the optimal distribution coefficient n of fine aggregate is mainly in the range of 0.15-0.58, so the distribution coefficient range is set to be 0.20-0.58 in the present case. Setting the length-diameter ratio parameter with reference to the real aggregate, wherein the setting range is 1.0-2.5;
(2) Utilizing the established three-dimensional ellipsoidal aggregate random throwing model to throw aggregates with different distribution coefficients and length-diameter ratios; fig. 3 shows graphs of random delivery results of ellipsoidal fine aggregates having aspect ratios of 1.0, 1.5, 2.0 and 2.5, respectively, with a distribution coefficient n of 0.37.
(3) Generating a random throwing result graph of ellipsoidal fine aggregate and storing corresponding void ratio and particle size distribution results; fig. 4 is a graph showing the change of the void ratio under different distribution coefficients, and it can be seen from the graph that the void ratio of ellipsoidal aggregates with different length-diameter ratios also changes along with the change of the distribution coefficient n, but the overall trend is the same, and the optimal distribution coefficient n exists so that the stacking system reaches the relative minimum void ratio. Meanwhile, as can be seen from the graph, the void ratio of the ellipsoidal aggregate with the length-diameter ratio of 1.0 is minimum, and when n=0.37, the void ratio of the stacking system is 20.11%; as the aspect ratio of the aggregate increases, the corresponding minimum void ratio also gradually increases, the void ratio of the ellipsoidal aggregate with the aspect ratio of 2.5 is the largest, and when n=0.44, the void ratio of the system is 26.29%, which is increased by 6.18% compared with the ellipsoidal aggregate with ar=1.0.
(4) The aspect ratio is cited as a characteristic factor, the correlation between the aspect ratio AR and the system porosity is calculated by combining the test results, and the test results are fitted; the data are screened and calculated, and the corresponding distribution coefficients and the void results of the aggregates under different length-diameter ratios AR are extracted to obtain the table 1. Table 1 shows the optimal distribution coefficient n and the corresponding void fraction results of the stacked system of ellipsoidal aggregates with different length-diameter ratios. As can be seen from table 1, as the length-diameter ratio parameter of the ellipsoidal aggregate increases, if the smallest system gap is desired to be formed to reach the optimal stacking state, the distribution coefficient n in the formula needs to be increased; meanwhile, as the optimal distribution coefficient n increases, the minimum void fraction of the corresponding system increases, when the length-diameter ratio AR=1.0, the optimal distribution coefficient n and the void fraction of the stacking system are respectively 0.37 and 20.11%, when the length-diameter ratio AR=2.50, the optimal distribution coefficient n and the void fraction of the stacking system are respectively 0.44 and 26.29%, and compared with the spherical aggregate, the void fraction is increased by 30.73%.
TABLE 1
From the above results, it is clear that the aspect ratio is the most relevant action effect parameter to the morphology of the aggregate particles, and the size thereof has an influence on the aggregate stacking system. Therefore, in order to better improve the application accuracy of the MAA model, the research quotes the length-diameter ratio AR as a characteristic factor, and the correlation between the length-diameter ratio AR and the system porosity is calculated by combining the test results in a fitting way, and the fitting result is shown in fig. 5.
The distribution coefficient n and the void fraction v of the aspect ratio AR are as follows o Is a calculation formula of (2).
n＝0.3225+0.05AR
v o ＝0.1924+0.007AR+0.014AR 2
According to the existing basic research, the MAA model corrected by the length-diameter ratio is shown as follows:
wherein: CPFT (%) is the cumulative sieve allowance smaller than the particle diameter D; d (D) L Indicating the maximum particle size in the stacking system; d (D) S Represents the smallest particle size in the particle system; d represents the current particle size; AR is the size of the longest axis to the shortest axis in the three axes of the aggregate in the stacking system, and AR is more than or equal to 1.
Step three, verification and comparison analysis of the corrected model:
(1) In the case of using natural quartz sand (NFA), the regenerated sand purchased from the building material market of the fertilizer-closing market, the grain size range is 0.15-4.75 mm, two kinds of regenerated sand with different length-diameter ratio sizes (RFA-1 and RFA-2) are crushed and generated by a jaw crusher, and the basic physical indexes of the natural sand (NFA) and the regenerated sand (RFA-1 and RFA-2) of two different production places are shown in the table 2.
TABLE 2
(2) Grading under different grading models is selected for proportion design; the test pieces can be classified into: group a (ar=1.0), group B (ar=1.5), group C (ar=2.0), group D (ar=2.5), group M (median grading zone interval), wherein 1, 2, 3 in each group classification represent natural sand, reclaimed sand 1, and reclaimed sand 2, respectively. The ratio of water, cement and fine aggregate in the five combination ratios is 1:0.65:3.53. table 3 shows the mass ratio of aggregate in different grain segments for five fine aggregate grading schemes selected in the test.
TABLE 3 Table 3
(3) Verification and comparison of void ratio, working performance and mechanical performance under different matching optimization schemes are carried out;
the results of the void fraction test for 15 different fine aggregate materials and the grading optimization scheme based on the aspect ratio AR modified MAA model are shown in FIG. 6.
As can be seen from FIG. 6, the void ratio of the dense packing of the natural sand with 5 design gradations is that the gradation A > gradation C > gradation D > gradation M > gradation B sequentially from high to low; according to the grain size distribution of different gradations, compared with the gradation M, in the gradation A with higher stacking void ratio, the fine aggregate particles with the grain size of 2.36-4.75 mm account for higher proportion, and the whole mass ratio is 31.5%; the fine aggregate particles with the particle size of 0.15-0.30 mm occupy smaller particle size, and the overall mass ratio is 11.3%; in the grading B with lower stacking void ratio, the whole grading distribution of 0.15-4.75 mm is more uniform. The dense packing void ratio of the 5 kinds of reclaimed sand-1 with designed grading is grade A > grade M > grade B > grade D > grade C from high to low, the dense packing void ratio of the grade C, grade D and grade B designed after the optimization of the modified MAA model can be seen from the figure, the dense packing void ratio of the grade M designed by adopting the median between two-level areas in the building sand specification is lower, compared with the distribution of different grade grain sections, the mass ratio of the grain sections of 2.36-4.75 mm in the grade C, grade D and grade B is higher, and the newly added coarse aggregate can effectively form a tighter packing structure with the grain section aggregate of 0.3-2.36 mm, so that the void ratio is reduced.
The dense stacking void ratio of the reclaimed sand-2 is graded M=graded A > graded B > graded C > graded D from high to low in sequence, and is consistent with the stacking void ratio law presented by the reclaimed sand-1, and along with the increase of the length-diameter ratio of the fine aggregate, the optimal stacking of the fine aggregate cannot be realized by adopting a traditional grading design method, and the void ratio of a stacking system can be reduced to a certain extent by using an improved MAA model for grading optimization.
The fluidity of the fresh mortar prepared from 5 kinds of natural sand and reclaimed sand with different grades is shown in fig. 7. As can be seen from FIG. 7, the fluidity of the 5 different graded natural sand designed by the test is changed from high to low from grade B > grade M > grade A > grade D > grade C, and the fluidity of the grade A, grade B, grade C and grade D are improved by-4.23%, 4.13%, 7.81% and-5.02% compared with the fluidity of the reference grade M; the fluidity of the reclaimed sand-1 is changed from high to low into grading C > grading D > grading M > grading B > grading A, and compared with the standard grading M, the fluidity of the grading A, the grading B, the grading C and the grading D is improved by-4.51 percent, -3.70 percent, 7.22 percent and 3.52 percent; the mobility of the reclaimed sand-2 is changed from high to low into grade D > grade C > grade B > grade M > grade A, and compared with the reference grade M, the mobility of the reclaimed sand-2 is improved by-1.91%, 1.28%, 1.91% and 9.76%; meanwhile, compared with the standard grading M, the quality ratio of 2.36-4.75 mm grain segments in the grading with the highest fluidity in the natural sand, the regenerated sand-1 and the regenerated sand-2 is respectively improved by 20.0 percent, 20.8 percent and 21.7 percent, and the fluidity of mortar is improved along with the increase of the content of larger-grain-size grains in the fine aggregate. The slurry is characterized in that the specific surface area of the fine aggregate is reduced along with the increase of the large-particle-size particles, the slurry required for wrapping the aggregate is reduced, the free slurry in the new mortar occupies a large volume, the slurry thickness among the aggregates is high, the internal friction force among the aggregates in the flowing process of the mortar can be obviously reduced, and the effect of improving the fluidity of the mortar is achieved.
The 28-day compressive strength of the new mortar prepared with 5 different grades of natural sand and reclaimed sand is shown in fig. 8. As shown in fig. 8, among 5 different gradations of the natural sand designed by the test, the mortar corresponding to the gradation B has the highest 28-day compressive strength reaching 36.6MPa, and the gradations of the gradation a and the gradation M have the lowest strength, namely the gradation C and the gradation D, and the gradation B has 2.52% higher 28-day compressive strength than the mortar of the reference gradation M; among 5 different gradations of the reclaimed sand-1, the 28-day compression strength of mortar corresponding to the gradation C is highest and reaches 33.7MPa, and then the gradations D and B are the gradations M and A, wherein the strength of the gradations is lowest, and the 28-day compression strength of the gradation C is improved by 12.0% compared with that of the mortar of the reference gradation M; among 5 different gradations of reclaimed sand-2, the mortar corresponding to gradation D has the highest 28-day compressive strength reaching 32.1MPa, and is secondarily graded C and B, wherein the gradations with the lowest strength are gradation M and gradation A, and the 28-day compressive strength of gradation C is improved by 9.6% compared with that of the mortar of the reference gradation M. The dense accumulation void ratio of natural sand and reclaimed sand in different gradations in the section 5.3.31 is combined, the change trend of 28-day compressive strength of different kinds of fine aggregates and mortar under the gradations has high correlation with the change of the dense accumulation void ratio of the fine aggregates, the lower the accumulation void ratio of the aggregates is, the higher the corresponding compressive strength of the mortar is, the machine-made sand gradation with lower accumulation void ratio can form a more stable skeleton structure in the mortar, and the compressive strength of the mortar is enhanced.
According to the invention, the particle morphology of the reclaimed sand is considered, the length-diameter ratio is taken as a characteristic factor, a numerical simulation method is adopted, the closest packing of the reclaimed sand particles with different length-diameter ratios is realized in a certain space range, and finally, the most widely applied Dinger-Funk equation (MAA model) in the existing continuous grading theory is corrected, so that a new reclaimed sand particle grading optimization method is obtained. Compared with the traditional method for optimizing the particle size distribution of the reclaimed sand, the method can consider the influence of the morphology of the reclaimed sand particles when the particle size distribution of the reclaimed sand is optimized, and obtain smaller void ratio and optimal particle size distribution.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (6)
1. A grading optimization method considering the morphology of reclaimed sand particles is characterized in that: the method comprises the following steps:
s1: establishing a three-dimensional ellipsoidal aggregate random throwing model;
s2: correcting the MAA model based on the morphology parameters of the reclaimed sand particles;
s3: and carrying out verification comparison analysis on the corrected model.
2. The grading optimization method considering morphology of reclaimed sand particles according to claim 1, wherein the method comprises the following steps: the step S1 specifically comprises the following steps:
s11: defining a space boundary and size range and grading distribution of aggregate;
s12: aggregate is put in a fixed space range, aggregate grading is put in according to the size from large to small, when the aggregate put volume in the size range is larger than the aggregate volume under the target grading, the aggregate put in the current grading range is ended, and the aggregate put in the next size range is carried out;
s13: randomly generating the centroid position of each aggregate, simultaneously randomly generating the triaxial size, the rotation matrix and the displacement matrix of the aggregate, and ensuring that the aggregate is in the boundary; the triaxial size of the aggregate needs to meet the particle size range of the aggregate;
s14: judging whether the aggregates accord with boundary conditions or not and judging the position relationship among the aggregates;
s15: and repeating the steps S11-S14 until the aggregate is successfully put in all the size ranges, and storing the geometric characteristics and the spatial position information of all the aggregates.
3. The grading optimization method considering morphology of reclaimed sand particles according to claim 1, wherein the method comprises the following steps: the step S2 specifically comprises the following steps:
s21: determining the particle size of the ellipsoidal aggregate put in the test, the distribution coefficient n in the MAA model and the range of the length-diameter ratio of the actual aggregate;
s22: utilizing the established three-dimensional ellipsoidal aggregate random throwing model to throw aggregates with different distribution coefficients and length-diameter ratios;
s23: generating a random throwing result graph of ellipsoidal fine aggregate and storing corresponding void ratio and particle size distribution results;
s24: and (3) taking the length-diameter ratio as a characteristic factor, and calculating the correlation between the length-diameter ratio AR and the system porosity by combining the test results in a fitting way, wherein the fitting result is obtained.
4. The grading optimization method considering morphology of reclaimed sand particles according to claim 3, wherein: in step S24, the distribution coefficient n of the aspect ratio AR and the void fraction v o The calculation formula of (2) is as follows:
n＝0.3225+0.05AR
v o ＝0.1924+0.007AR+0.014AR 2 。
5. the grading optimization method considering morphology of reclaimed sand particles according to claim 3, wherein: the MAA model corrected by the length-diameter ratio is shown as follows:
wherein: CPFT (%) is the cumulative sieve allowance smaller than the particle diameter D; d (D) L Indicating the maximum particle size in the stacking system; d (D) S Represents the smallest particle size in the particle system; d represents the current particle size; AR is the size of the longest axis to the shortest axis in the three axes of the aggregate in the stacking system, and AR is more than or equal to 1.
6. The grading optimization method considering morphology of reclaimed sand particles according to claim 1, wherein the method comprises the following steps: the step S3 specifically comprises the following steps:
s31: determining parameters of the modified model and raw material attribute parameters;
s32: grading under different grading models is selected for proportion design;
s34: and verifying and comparing the void ratio, the working performance and the mechanical performance under different matching optimization schemes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310602978.4A CN116663378B (en) | 2023-05-23 | 2023-05-23 | Grading optimization method considering morphology of reclaimed sand particles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310602978.4A CN116663378B (en) | 2023-05-23 | 2023-05-23 | Grading optimization method considering morphology of reclaimed sand particles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116663378A true CN116663378A (en) | 2023-08-29 |
| CN116663378B CN116663378B (en) | 2024-06-04 |
Family
ID=87712984
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310602978.4A Active CN116663378B (en) | 2023-05-23 | 2023-05-23 | Grading optimization method considering morphology of reclaimed sand particles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116663378B (en) |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140245925A1 (en) * | 2011-10-17 | 2014-09-04 | Research Institute Of Highway, Mot, Prc | Mix composition determination method of coarse graded high modulus asphalt concrete with skeleton embedded structure |
| US20140257742A1 (en) * | 2011-10-15 | 2014-09-11 | Research Institute Of Highway, Mot, Prc | Method to structure mineral aggregate gradation by using three control points and two curves |
| WO2017052481A1 (en) * | 2015-09-23 | 2017-03-30 | Ouypornorasert Winai | A method to find concrete mix proportion by minimum void in aggregates and sharing of cement paste |
| CN110210178A (en) * | 2019-06-26 | 2019-09-06 | 西安理工大学 | A kind of construction method based on Python regeneration concrete three-dimensional random spherical shape aggregate model |
| KR102036896B1 (en) * | 2019-03-08 | 2019-11-26 | (주)한동알앤씨 | Mixed Aggregate for Subbase Layer Using Recycled Asphalt Concrete and Soil and Manufacturing Method Thereof |
| CN112632814A (en) * | 2020-12-04 | 2021-04-09 | 西安理工大学 | Method for constructing three-dimensional model of recycled concrete based on ellipsoid random aggregate |
| CN112669917A (en) * | 2020-12-24 | 2021-04-16 | 南昌工程学院 | Regenerative RAP concrete mix proportion design method based on stacking compaction method |
| CN113077852A (en) * | 2021-03-26 | 2021-07-06 | 山东省路桥集团有限公司 | Cement stabilized macadam mineral aggregate grading optimization method based on waste concrete regenerated aggregate |
| CN114925590A (en) * | 2022-06-24 | 2022-08-19 | 中南大学 | Aggregate generation algorithm and construction method of reclaimed water stable aggregate discrete element numerical model |
| CN115050431A (en) * | 2022-04-27 | 2022-09-13 | 中南大学 | Modeling analysis method for three-dimensional microscopic structure of cement stabilized recycled aggregate |
| CN115340348A (en) * | 2022-09-15 | 2022-11-15 | 上海市建筑科学研究院有限公司 | Low-carbon composite cementing material stable road solid waste inorganic mixture and preparation method and application thereof |
| CN115482891A (en) * | 2022-09-26 | 2022-12-16 | 合肥工业大学 | Construction method of recycled concrete mesoscopic random aggregate model based on convex polygon |
| CN115690316A (en) * | 2022-11-01 | 2023-02-03 | 合肥工业大学 | Aggregate three-dimensional reconstruction and random generation method based on spherical DOG wavelet |
| CN115900586A (en) * | 2022-11-24 | 2023-04-04 | 合肥工业大学 | Reclaimed sand particle morphology real-time monitoring device and real-time monitoring method |
| CN115925342A (en) * | 2022-12-28 | 2023-04-07 | 福建省交通科研院有限公司 | Method for optimizing mix proportion of in-situ broken recycled water stabilization layer of old cement concrete on road surface |
| CN116754587A (en) * | 2023-06-21 | 2023-09-15 | 合肥工业大学 | Quantitative characterization method for distribution of residual mortar layer on surface of recycled aggregate in three-dimensional layer |
-
2023
- 2023-05-23 CN CN202310602978.4A patent/CN116663378B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140257742A1 (en) * | 2011-10-15 | 2014-09-11 | Research Institute Of Highway, Mot, Prc | Method to structure mineral aggregate gradation by using three control points and two curves |
| US20140245925A1 (en) * | 2011-10-17 | 2014-09-04 | Research Institute Of Highway, Mot, Prc | Mix composition determination method of coarse graded high modulus asphalt concrete with skeleton embedded structure |
| WO2017052481A1 (en) * | 2015-09-23 | 2017-03-30 | Ouypornorasert Winai | A method to find concrete mix proportion by minimum void in aggregates and sharing of cement paste |
| KR102036896B1 (en) * | 2019-03-08 | 2019-11-26 | (주)한동알앤씨 | Mixed Aggregate for Subbase Layer Using Recycled Asphalt Concrete and Soil and Manufacturing Method Thereof |
| CN110210178A (en) * | 2019-06-26 | 2019-09-06 | 西安理工大学 | A kind of construction method based on Python regeneration concrete three-dimensional random spherical shape aggregate model |
| CN112632814A (en) * | 2020-12-04 | 2021-04-09 | 西安理工大学 | Method for constructing three-dimensional model of recycled concrete based on ellipsoid random aggregate |
| CN112669917A (en) * | 2020-12-24 | 2021-04-16 | 南昌工程学院 | Regenerative RAP concrete mix proportion design method based on stacking compaction method |
| CN113077852A (en) * | 2021-03-26 | 2021-07-06 | 山东省路桥集团有限公司 | Cement stabilized macadam mineral aggregate grading optimization method based on waste concrete regenerated aggregate |
| CN115050431A (en) * | 2022-04-27 | 2022-09-13 | 中南大学 | Modeling analysis method for three-dimensional microscopic structure of cement stabilized recycled aggregate |
| CN114925590A (en) * | 2022-06-24 | 2022-08-19 | 中南大学 | Aggregate generation algorithm and construction method of reclaimed water stable aggregate discrete element numerical model |
| CN115340348A (en) * | 2022-09-15 | 2022-11-15 | 上海市建筑科学研究院有限公司 | Low-carbon composite cementing material stable road solid waste inorganic mixture and preparation method and application thereof |
| CN115482891A (en) * | 2022-09-26 | 2022-12-16 | 合肥工业大学 | Construction method of recycled concrete mesoscopic random aggregate model based on convex polygon |
| CN115690316A (en) * | 2022-11-01 | 2023-02-03 | 合肥工业大学 | Aggregate three-dimensional reconstruction and random generation method based on spherical DOG wavelet |
| CN115900586A (en) * | 2022-11-24 | 2023-04-04 | 合肥工业大学 | Reclaimed sand particle morphology real-time monitoring device and real-time monitoring method |
| CN115925342A (en) * | 2022-12-28 | 2023-04-07 | 福建省交通科研院有限公司 | Method for optimizing mix proportion of in-situ broken recycled water stabilization layer of old cement concrete on road surface |
| CN116754587A (en) * | 2023-06-21 | 2023-09-15 | 合肥工业大学 | Quantitative characterization method for distribution of residual mortar layer on surface of recycled aggregate in three-dimensional layer |
Non-Patent Citations (16)
| Title |
|---|
| CHU, HY (CHU, HONGYAN); QIN, JJ (QIN, JIANJIAN); GAO, L (GAO, LI); JIANG, JY (JIANG, JINYANG); WANG, FJ (WANG, FENGJUAN) ; WANG, D: "Effects of graphene oxide on mechanical properties and microstructure of ultra-high-performance lightweight concrete", WEB OF SCIENCE, vol. 12, no. 6, 3 June 2023 (2023-06-03), pages 647 - 660 * |
| YANG, R (YANG, RUI)等: "The physical and chemical impact of manufactured sand as a partial replacement material in Ultra-High Performance Concrete (UHPC)", WEB OF SCIENCE, vol. 99, 31 May 2019 (2019-05-31), pages 203 - 213, XP085659987, DOI: 10.1016/j.cemconcomp.2019.03.020 * |
| 余睿;范定强;水中和;王鑫鹏;: "基于颗粒最紧密堆积理论的超高性能混凝土配合比设计", 硅酸盐学报, no. 08, 23 June 2020 (2020-06-23) * |
| 吴亚俊;詹炳根;程新;: "高性能聚苯颗粒夹芯内隔墙板芯材的研制与性能", 墙材革新与建筑节能, no. 12, 10 December 2016 (2016-12-10) * |
| 宋福申;薛飞;米金玲;王永霖;刘琬真;崔艳玲;鲁行之;: "建筑垃圾再生砖砂的性能特征研究", 河南建材, no. 02, 10 April 2019 (2019-04-10) * |
| 张前江，洪丽，高鹏，蒋津，陈亚迪: "聚合物改性再生骨料混凝土力学性能试验研究", 混凝土与水泥制品, 20 June 2018 (2018-06-20) * |
| 李哲;龙广成;谢友均;: "再生橡胶颗粒对自流平砂浆力学性能影响的试验研究", 材料导报, no. 2, 25 November 2011 (2011-11-25) * |
| 李振龙;李颖;吴春丽;: "再生细骨料制备陶粒混凝土抗压强度与导热系数试验研究", 广东建材, no. 03, 15 March 2017 (2017-03-15) * |
| 杜晓武;杨晓炳;王永定;高谦;: "改善骨料级配的AQH堆积模型", 矿业研究与开发, no. 01, 25 January 2020 (2020-01-25) * |
| 潘丽云;梁娜;胡飞佳;姚坤奇;程丹丹;裴松伟;: "全再生粗骨料混凝土力学性能试验与评价研究", 华北水利水电大学学报(自然科学版), no. 06, 15 December 2017 (2017-12-15) * |
| 申轶男;陈华辉;胡宇;: "原料颗粒级配对多孔陶瓷性能的影响", 材料研究学报, no. 05, 25 October 2011 (2011-10-25) * |
| 胡锋;范定强;王雄江;刘潇;白家乐;王佳盟;: "生态型超高性能混凝土在简支梁桥中的应用研究", 城市建筑, no. 28, 5 October 2019 (2019-10-05) * |
| 赵云;于献青;袁静;赵羽习;沈林昌;陆建昶;王国祥;: "实际生产再生细骨料试验与性能分析", 混凝土, no. 09, 27 September 2016 (2016-09-27) * |
| 郑蓉美;詹炳根;张青峰;: "颗粒分布对粉煤灰调粒水泥强度的影响", 合肥工业大学学报(自然科学版), no. 05, 28 May 2009 (2009-05-28) * |
| 陈永志;: "废玻璃对排水沥青混合料路用性能影响研究", 公路工程, no. 02, 20 April 2015 (2015-04-20) * |
| 高丹盈;张丽娟;芦静云;闫兆强;: "再生骨料混凝土配合比设计参数研究", 建筑科学与工程学报, no. 01, 15 January 2016 (2016-01-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116663378B (en) | 2024-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106498898B (en) | A kind of earth and stone material Gradation Optimization method based on fractal theory and physical mechanics test | |
| CN107391790B (en) | Green self-compacting concrete and preparation method thereof | |
| CN111781051A (en) | Artificial conglomerate core based on lithology and permeability control and preparation method and application thereof | |
| CN106544944A (en) | A kind of small particle framework compact type asphalt mixture grading method | |
| CN110318319B (en) | Thermal regeneration asphalt mixture grading design method based on orthogonal design | |
| CN112592106B (en) | Design method for sand grain type iron tailing asphalt mixture mixing proportion | |
| CN110194612B (en) | Recycled asphalt concrete material capable of guaranteeing low-temperature performance and design method thereof | |
| CN116663378B (en) | Grading optimization method considering morphology of reclaimed sand particles | |
| CN111272631A (en) | Improved sandstone reservoir pore structure quantitative characterization method | |
| CN109776007B (en) | Method for determining optimal gradation of artificial filler | |
| CN115050431B (en) | Modeling analysis method for three-dimensional microstructure of cement stabilized recycled aggregate | |
| CN111339680A (en) | Grading composition design method for large-particle-size graded crushed stone | |
| CN106116281B (en) | Coral reef sand concrete and preparation method thereof | |
| Shi et al. | Research on the characteristics of asphalt mixture gradation curve based on Weibull distribution | |
| CN103616312A (en) | Method for measuring and calculating maximum dry density of natural debris | |
| CN111470806B (en) | Large-particle-size aggregate core wall asphalt concrete and mixing proportion optimization method thereof | |
| CN110502854A (en) | A kind of preparation method of alternate resources concrete | |
| CN116386771A (en) | Design method of low-carbon cement stabilized macadam material | |
| CN116029021A (en) | Structure and material integration-based cement stabilized iron tailing base crack-resistant mixing proportion design method | |
| CN109766636B (en) | Asphalt mixture gradation design method based on particle accumulation theory | |
| CN104502529B (en) | Expansible perlite model sasnd and preparation method thereof | |
| CN106777892A (en) | A kind of steel slag asphalt compound mineral aggregate gradation design method | |
| Ogundipe et al. | Impact of Partial Replacement of granite with periwinkle and palm kernel shells on concrete strength | |
| Jeon et al. | Comprehensive laboratory testing and performance evaluation of recycled pulverized hot-mix asphalt material | |
| Liu et al. | Research on roller compacted concrete graduation with vibration liquefaction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |