CN114662374B - Method for identifying contour evolution characteristics of construction waste roadbed filler particles in mechanical test - Google Patents
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- 239000002245 particle Substances 0.000 title claims abstract description 123
- 239000000945 filler Substances 0.000 title claims abstract description 66
- 238000012360 testing method Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000010276 construction Methods 0.000 title claims abstract description 20
- 239000002699 waste material Substances 0.000 title claims abstract description 19
- 238000002474 experimental method Methods 0.000 claims abstract description 13
- 239000008187 granular material Substances 0.000 claims description 19
- 239000011449 brick Substances 0.000 claims description 18
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Abstract
The invention discloses a method for identifying the outline evolution characteristics of building rubbish roadbed filler particles in a mechanical test, which comprises the following steps: s1: acquiring a pellet arrangement image of the regenerated filler; s2: obtaining shape parameters of each particle in each group of particle files; s3: respectively obtaining fractal dimension, abundance, circularity, shape coefficient and crushing rate of each particle in each group of particle files after a mechanical experiment; s4: calculating the average value of the shape parameters of each particle before and after a mechanical experiment to obtain the shape profile evolution of different particles in each group of particle files; s5: and calculating the association degree of different indexes and the crushing rate, and taking the parameter corresponding to the maximum association degree as the factor with the largest influence on the crushing of the regenerated filler. The invention can effectively analyze the contour evolution characteristics of different components in the regenerated filler before and after a mechanical test, can deeply disclose the crushing mechanism of the different components, and can promote the cognition of the road performance of the construction waste filler.
Description
Technical Field
The invention belongs to the technical field of contour evolution, and particularly relates to a method for identifying contour evolution characteristics of construction waste roadbed filler particles in a mechanical test.
Background
The construction waste is used for roadbed filling, and has the problems that the construction waste contains more soft components such as bricks and the like, and secondary crushing is easy to occur in the construction and operation processes, so that larger sedimentation deformation is caused. So that the research on the secondary crushing characteristics of each component in the regenerated filler becomes extremely necessary. The conventional method is to screen the regenerated filler before and after the mechanical test respectively, so that the change trend of the filler grading can be only obtained, and the granule with which characteristic parameter is easier to crush and the degree of crushing cannot be specifically analyzed. If Xu Pengcheng, screening and manual sorting are respectively carried out on the recycled mixed aggregate before and after crushing in the process of determining the aggregate grading determination method for the road surface base layer or the subbase layer and the recycled concrete brick mixed aggregate crushing fractal rule, and a series of changes of the quality, the volume and the like of the recycled mixed aggregate before and after crushing are explored. The existing method only can obtain the variation trend of the quality and the volume of different particle fillers, and cannot carry out detailed quantitative analysis and detailed description on the specific crushing behavior and shape contour evolution of each particle filler.
Disclosure of Invention
The invention provides a method for identifying the outline evolution characteristics of the roadbed filler particles of the construction waste in a mechanical test in order to solve the problems.
The technical scheme of the invention is as follows: the method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in the mechanical test comprises the following steps:
S1: screening the regenerated filler to obtain a granule arrangement image of the regenerated filler;
S2: according to the granule arrangement image of the regenerated filler, the shape parameters of each granule in each group of granule files are obtained;
s3: carrying out a mechanical experiment on the regenerated filler, and respectively obtaining fractal dimension, abundance, circularity, shape coefficient and crushing rate of each particle in each group of particle files after the mechanical experiment;
s4: calculating the average value of the shape parameters of each particle before and after a mechanical experiment to obtain the shape profile evolution of different particles in each group of particle files;
S5: based on the change sequence of the breaking rate, the fractal dimension, the abundance, the circularity and the shape factor, calculating the association degree of different indexes and the breaking rate, taking the parameter corresponding to the maximum association degree as the factor with the largest influence on the breaking of the regenerated filler, and taking the shape contour evolution of different particles in each group of particle files and the factor with the largest influence on the breaking of the regenerated filler as the shape contour evolution result.
Further, in step S1, the specific method for collecting the pellet arrangement image is as follows: screening the regenerated filler with different particle sizes to obtain each group of particle files; dividing the regenerated filler of each group of grain files into four parts, and randomly sampling from each group of grain files; and acquiring sample images of each group of grain files by using a camera to obtain grain arrangement images.
Further, in step S2, the granules include bricks, concrete blocks and stones;
the shape parameters comprise abundance C, circularity R and shape factor F of the particles, and the calculation formulas are respectively as follows:
Wherein A represents the actual area of the particle, B represents the minor axis size of the particle, L represents the major axis size of the particle, A' represents the area of the circumscribing circle of the particle, P represents the circumference of the area equal to the particle, and S represents the actual circumference of the particle.
Further, in step S3, the mechanical experiment causes the particles to be broken, so that the shape profile of each particle is changed.
Further, in step S3, the calculation formula of the crushing rate B g of each particle file is:
Bg＝Σ|ΔWk|
ΔWk＝Wki-Wkf
Wherein W ki represents the content of the particle size on the particle size distribution curve before the mechanical test, W kf represents the content of the same particle size on the particle size distribution curve after the mechanical test, and DeltaW k represents the absolute difference of the particle size content before and after the test.
Further, step S5 comprises the sub-steps of:
s51: taking the breaking rate of each particle file as a parent sequence, taking the fractal dimension as a first characteristic sequence, taking the abundance as a second characteristic sequence, taking the circularity as a third characteristic sequence and taking the shape factor as a fourth characteristic sequence;
S52: carrying out dimensionless treatment on the mother sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating association coefficients between each characteristic sequence and the mother sequence after the dimensionless tempering;
s53: and calculating the average value of the association coefficients between each characteristic sequence and the parent sequence, and taking the average value as the association degree.
Further, in step S51, the expression of the parent sequence X b is X b＝{xb (k) |k=1, 2, … }, the expression of the first feature sequence X w is X w＝{xw (k) |k=1, 2, … }, the expression of the second feature sequence X is X c＝{xc (k) |k=1, 2, … }, the expression of the third feature sequence X r is X r＝{xr (k) |k=1, 2, … }, and the expression of the fourth feature sequence X f is X f＝{xf (k) |k=1, 2, … }; wherein x b (k) represents each crush rate value, x w (k) represents each fractal dimension value, x c (k) represents each abundance value, x r (k) represents each circularity value, and x f (k) represents each shape coefficient value;
In step S52, the non-dimensionality processing is performed on each sequence, and the X i sequence is converted into the Y i sequence, where the calculation formula is as follows:
Wherein: y i (k) represents the data of the sequence Y i after dimensionless treatment, X i (k) represents the data of the sequence X i, X i (l) represents the average value of a certain sequence, N represents the number of sequence data;
In step S52, the calculation formula of the association coefficient ζ i (k) between different feature sequences and the parent sequence is:
Wherein Δ b,i (k) represents the absolute difference of two sequences at time k, Δ max (b, i) and Δ min (b, i) are the maximum value and the minimum value in the absolute differences at each time, ρ represents the resolution factor, w represents the fractal dimension, c represents the abundance, r represents the circularity, and f represents the shape coefficient value;
In step S53, the calculation formula of the average value γ b,i of the correlation coefficients between each feature sequence and the parent sequence is:
where n represents the sequence length.
The beneficial effects of the invention are as follows: most of the prior methods for screening the regenerated filler before and after the mechanical test are adopted, only the grading change of the regenerated filler before and after the mechanical test can be obtained, the shape evolution rule of different particles before and after the mechanical test can not be revealed, and the crushing behaviors of different components can not be revealed. The invention can effectively analyze the contour evolution characteristics of different components in the regenerated filler before and after a mechanical test, can deeply disclose the crushing mechanism of the different components, and can promote the cognition of the road performance of the construction waste filler.
Drawings
FIG. 1 is a flow chart of a method for identifying the outline evolution characteristics of the construction waste roadbed filler particles;
FIG. 2 is an image of reclaimed filler GS 20～40 -1, 2, 3, 4;
FIG. 3 is a tracing of the contour of a brick using an AOI tool;
FIG. 4 is a process diagram of selecting a measurement item and taking a measurement;
FIG. 5 is a graph of evaluation index coefficients for calculating the shape change of each component in the reclaimed filler based on the output measurement results;
fig. 6 is a graph of distribution characteristics of brick abundance before and after a mechanical test.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in a mechanical test, which comprises the following steps:
S1: screening the regenerated filler to obtain a granule arrangement image of the regenerated filler;
S2: according to the granule arrangement image of the regenerated filler, the shape parameters of each granule in each group of granule files are obtained;
s3: carrying out a mechanical experiment on the regenerated filler, and respectively obtaining fractal dimension, abundance, circularity, shape coefficient and crushing rate of each particle in each group of particle files after the mechanical experiment;
s4: calculating the average value of the shape parameters of each particle before and after a mechanical experiment to obtain the shape profile evolution of different particles in each group of particle files;
S5: based on the change sequence of the breaking rate, the fractal dimension, the abundance, the circularity and the shape factor, calculating the association degree of different indexes and the breaking rate, taking the parameter corresponding to the maximum association degree as the factor with the largest influence on the breaking of the regenerated filler, and taking the shape contour evolution of different particles in each group of particle files and the factor with the largest influence on the breaking of the regenerated filler as the shape contour evolution result.
In the embodiment of the present invention, in step S1, a specific method for acquiring an arrangement image of the granules is as follows: screening the regenerated filler with different particle sizes to obtain each group of particle files; dividing the regenerated filler of each group of grain files into four parts, and randomly sampling from each group of grain files; and acquiring sample images of each group of grain files by using a camera to obtain grain arrangement images.
In the embodiment of the invention, the regenerated filler before the mechanical test is screened and different particle grades are weighed and recorded. The particle size range of the filler screening can be selected according to the actual condition of the material, and three particle sizes of 0-10 mm, 10-20 mm and 20-40 mm are taken as examples. Earlier studies showed that the profile recognition error was large when the particles were too fine and considering that the fine particles were generally stable, it was suggested to take 10mm as a boundary and profile analysis was performed for particles larger than 10 mm.
Random sampling: the filler of each pellet group was aliquoted and one portion was randomly sampled in each aliquot, i.e., four portions were taken in parallel for each pellet group, which was designated GS 10～20-i,GS20～40 -i, respectively. The sampling link ensures that the particles in each sample are about 100 particles. In GS 10～20 -i, 10-20 mm represents the particle size range; i=1 to 4; GS is an acronym for Grain Size. Taking GS 10～20 -1 as an example, it represents sample 1 of the 10-20 mm granule.
A white board with a reference scale is placed on a desktop or the ground, a high-definition camera is fixed right above the white board, and the camera angle is adjusted. And adjusting shooting parameters of the camera before shooting so as to ensure the optimal shooting effect. The camera position and camera shooting parameters in the same particle file filling shooting process are kept unchanged. Taking GS 10～20 -1 as an example, filler particles in a sample are randomly placed on the white board in the step 3, and the arrangement is to ensure that the material distribution is random and not overlapped and contacted with each other. Then manually identifying the components such as bricks, cement mortar, concrete blocks, stones and the like in the sample, and numbering each particle. And photographing each arranged sample to obtain a granule arrangement image. The photographs taken were imported into Image-Pro Plus (IPP) software. The measuring system in the software is calibrated by means of the ruler on the paperboard to ensure that the actual size and contour characteristics of the filler particles can be measured.
In the embodiment of the present invention, in step S2, the particles include bricks, concrete blocks and stones;
the shape parameters comprise abundance C, circularity R and shape factor F of the particles, and the calculation formulas are respectively as follows:
Wherein A represents the actual area of the particle, B represents the minor axis size of the particle, L represents the major axis size of the particle, A' represents the area of the circumscribing circle of the particle, P represents the circumference of the area equal to the particle, and S represents the actual circumference of the particle.
In an embodiment of the invention, each pellet is profiled with an AOI tool and a measurement object is generated by convert AOI to object. The shape parameters of the area (A), perimeter (S), long axis dimension (L), short axis dimension (B) and the like of each particle are automatically measured in the system.
In the embodiment of the present invention, in step S3, the mechanical experiment causes the particles to be broken, so that the shape profile of each particle is changed.
In the embodiment of the present invention, in step S3, the calculation formula of the crushing rate B g of each particle file is:
Bg＝Σ|ΔWk|
ΔWk＝Wki-Wkf
Wherein W ki represents the content of the particle size on the particle size distribution curve before the mechanical test, W kf represents the content of the same particle size on the particle size distribution curve after the mechanical test, and DeltaW k represents the absolute difference of the particle size content before and after the test.
In the embodiment of the invention, the shape parameters of each particle of the mechanical test are counted to obtain the distribution intervals and the average values of different parameters under different components, and the average values of the parameters before and after the test are compared and analyzed to obtain the change rule of the parameters under the mechanical test. And then, according to the component characteristics, the evolution characteristics of the particle shape outlines of different components in each particle file before and after the mechanical test can be obtained.
Taking the abundance of a brick as an example, it indicates how flat the brick is, the smaller the value thereof, the closer it is to the needle shape the surface, the more the value thereof tends to be 1, the more the major and minor axes tend to be equal. The distribution interval of the parameter represents the distribution of the main oblate degree of the brick in the current state, and the change rule of the oblate degree of the brick in the mechanical test can be reflected by comparing the abundance of the brick before and after the mechanical test. Multiple tests can obtain a value that the abundance tends to be relatively fixed, namely, under what oblate degree the brick tends to be stable. The evolution rule of other parameters of other components can be obtained.
In an embodiment of the present invention, step S5 includes the sub-steps of:
s51: taking the breaking rate of each particle file as a parent sequence, taking the fractal dimension as a first characteristic sequence, taking the abundance as a second characteristic sequence, taking the circularity as a third characteristic sequence and taking the shape factor as a fourth characteristic sequence;
S52: carrying out dimensionless treatment on the mother sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating association coefficients between each characteristic sequence and the mother sequence after the dimensionless tempering;
s53: and calculating the average value of the association coefficients between each characteristic sequence and the parent sequence, and taking the average value as the association degree.
In the embodiment of the present invention, in step S51, the expression of the parent sequence X b is X b＝{xb (k) |k=1, 2, … }, the expression of the first feature sequence X w is X w＝{xw (k) |k=1, 2, … }, the expression of the second feature sequence X is X c＝{xc (k) |k=1, 2, … }, the expression of the third feature sequence X r is X r＝{xr (k) |k=1, 2, … }, and the expression of the fourth feature sequence X f is X f＝{xf (k) |k=1, 2, … }; wherein x b (k) represents each crush rate value, x w (k) represents each fractal dimension value, x c (k) represents each abundance value, x r (k) represents each circularity value, and x f (k) represents each shape coefficient value;
In step S52, the non-dimensionality processing is performed on each sequence, and the X i sequence is converted into the Y i sequence, where the calculation formula is as follows:
Wherein: y i (k) represents the data of the sequence Y i after dimensionless treatment, X i (k) represents the data of the sequence X i, X i (l) represents the average value of a certain sequence, N represents the number of sequence data;
In step S52, the calculation formula of the association coefficient ζ i (k) between different feature sequences and the parent sequence is:
Wherein Δ b,i (k) represents the absolute difference of two sequences at time k, Δ max (b, i) and Δ min (b, i) are the maximum value and the minimum value in the absolute differences at each time, ρ represents the resolution factor, w represents the fractal dimension, c represents the abundance, r represents the circularity, and f represents the shape coefficient value;
In step S53, the calculation formula of the average value γ b,i of the correlation coefficients between each feature sequence and the parent sequence is:
where n represents the sequence length.
In the embodiment of the invention, main indexes of different components influencing the breakage rate are analyzed, and fractal dimension, abundance, circularity and shape factor respectively represent the fractal degree, flat roundness, circularity and complexity of the outline of the particles, and all the indexes influence whether the particles are easy to break or not. However, due to the difference in composition, there may be a difference in the index of the degree of difficulty in crushing for different component particles. For different components, sorting the association degree of the characteristic sequence and the parent sequence obtained by calculation, wherein the index with the maximum association degree is the factor which is most easy to influence the crushing of the components.
The invention will now be described with reference to specific examples. The mechanical test performed in this embodiment is vibration compaction, and the rating index for evaluating the shape evolution rule of each component of the regenerated filler includes: abundance, circularity, shape factor. Wherein, fig. 2 is an image of the regenerated filler GS 20～40 -1,2, 3,4, fig. 3 is a trace drawing of the outline of the brick using an AOI tool, fig. 4 is a process of selecting and measuring the measurement items including Aspect, axis (major), axis (minor), PERIMETER, AREA (polygon), fig. 5 is an evaluation index coefficient of shape change of each component in the regenerated filler calculated according to the output measurement result, and fig. 6 is a distribution characteristic of abundance of the brick before and after the mechanical test. Table 1 shows the output measurement results, table 2 shows the calculated average value of the index system, and Table 3 shows the calculated correlation between each index of the different components and the crushing rate. From the analysis results, it can be seen that: after vibration compaction, the abundance coefficient of stone blocks, brick blocks and concrete blocks is obviously increased, and the abundance of the compacted particles is closer to 1; in the aspect of circularity, stone blocks and bricks are obviously changed, and granules with smaller circularity are easier to break; the shape factor of the stone and brick is also increased significantly after compaction.
TABLE 1
TABLE 2
TABLE 3 Table 3
The beneficial effects of the invention are as follows: most of the prior methods for screening the regenerated filler before and after the mechanical test are adopted, only the grading change of the regenerated filler before and after the mechanical test can be obtained, the shape evolution rule of different particles before and after the mechanical test can not be revealed, and the crushing behaviors of different components can not be revealed. The invention can effectively analyze the contour evolution characteristics of different components in the regenerated filler before and after a mechanical test, can deeply disclose the crushing mechanism of the different components, and can promote the cognition of the road performance of the construction waste filler.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (7)
1. The method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in the mechanical test is characterized by comprising the following steps of:
S1: screening the regenerated filler to obtain a granule arrangement image of the regenerated filler;
S2: according to the granule arrangement image of the regenerated filler, the shape parameters of each granule in each group of granule files are obtained;
s3: carrying out a mechanical experiment on the regenerated filler, and respectively obtaining fractal dimension, abundance, circularity, shape coefficient and crushing rate of each particle in each group of particle files after the mechanical experiment;
s4: calculating the average value of the shape parameters of each particle before and after a mechanical experiment to obtain the shape profile evolution of different particles in each group of particle files;
S5: based on the change sequence of the breaking rate, the fractal dimension, the abundance, the circularity and the shape factor, calculating the association degree of different indexes and the breaking rate, taking the parameter corresponding to the maximum association degree as the factor with the largest influence on the breaking of the regenerated filler, and taking the shape contour evolution of different particles in each group of particle files and the factor with the largest influence on the breaking of the regenerated filler as the shape contour evolution result.
2. The method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in the mechanical test according to claim 1, wherein in the step S1, the specific method for collecting the granule arrangement image is as follows: screening the regenerated filler with different particle sizes to obtain each group of particle files; dividing the regenerated filler of each group of grain files into four parts, and randomly sampling from each group of grain files; and acquiring sample images of each group of grain files by using a camera to obtain grain arrangement images.
3. The method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in the mechanical test according to claim 1, wherein in the step S2, the particles comprise bricks, concrete blocks and stones;
the shape parameters comprise abundance C, circularity R and shape factor F of the particles, and the calculation formulas are respectively as follows:
Wherein A represents the actual area of the particle, B represents the minor axis size of the particle, L represents the major axis size of the particle, A' represents the area of the circumscribing circle of the particle, P represents the circumference of the area equal to the particle, and S represents the actual circumference of the particle.
4. The method for identifying the profile evolution characteristics of the particles of the roadbed filler of the construction waste in the mechanical test according to claim 1, wherein in the step S3, the mechanical test causes the particles to be broken, so that the shape profile of each particle is changed.
5. The method for identifying the profile evolution characteristics of the construction waste roadbed filler particles in the mechanical test according to claim 1, wherein in the step S3, the calculation formula of the breaking rate B g of each particle file is as follows:
Bg＝∑|ΔWk|
ΔWk＝Wki-Wkf
Wherein W ki represents the content of the particle size on the particle size distribution curve before the mechanical test, W kf represents the content of the same particle size on the particle size distribution curve after the mechanical test, and DeltaW k represents the absolute difference of the particle size content before and after the test.
6. The method for identifying the profile evolution characteristics of the particles of the roadbed filler of the construction waste in the mechanical test according to claim 1, wherein the step S5 comprises the following substeps:
s51: taking the breaking rate of each particle file as a parent sequence, taking the fractal dimension as a first characteristic sequence, taking the abundance as a second characteristic sequence, taking the circularity as a third characteristic sequence and taking the shape factor as a fourth characteristic sequence;
S52: carrying out dimensionless treatment on the mother sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating association coefficients between each characteristic sequence and the mother sequence after the dimensionless tempering;
s53: and calculating the average value of the association coefficients between each characteristic sequence and the parent sequence, and taking the average value as the association degree.
7. The method for identifying the outline evolution characteristics of the construction waste roadbed filler particles in the mechanical test according to claim 6, wherein in the step S51, the expression of the parent sequence X b is X b＝{xb (k) |k=1, 2..the expression of the first feature sequence X w is X w＝{xw (k) |k=1, 2..the expression of the second feature sequence X is xc= { X c (k) |k=1, 2..the expression of the third feature sequence X r is X r＝{xr (k) |k=1, 2..the expression of the fourth feature sequence X f is X f＝{xf (k) |k=1, 2..; wherein x b (k) represents each crush rate value, x w (k) represents each fractal dimension value, x c (k) represents each abundance value, x r (k) represents each circularity value, and x f (k) represents each shape coefficient value;
in the step S52, the non-dimensionality processing is performed on each sequence, and the X i sequence is converted into the Y i sequence, and the calculation formula is as follows:
Wherein: y i (k) represents the data of the sequence Y i after dimensionless treatment, X i (k) represents the data of the sequence X i, X i (l) represents the average value of a certain sequence, N represents the number of sequence data;
in the step S52, the calculation formula of the association coefficient ζ i (k) between the different feature sequences and the parent sequence is:
Wherein Δ b,i (k) represents the absolute difference of two sequences at time k, Δ max (b, i) and Δ min (b, i) are the maximum value and the minimum value in the absolute differences at each time, ρ represents the resolution factor, w represents the fractal dimension, c represents the abundance, r represents the circularity, and f represents the shape coefficient value;
in the step S53, a calculation formula of the average value γ b,i of the correlation coefficient between each feature sequence and the parent sequence is as follows:
where n represents the sequence length.
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| CN202210281555.2A CN114662374B (en) | 2022-03-21 | 2022-03-21 | Method for identifying contour evolution characteristics of construction waste roadbed filler particles in mechanical test |
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