CN114662374B - Method for identifying contour evolution characteristics of construction waste roadbed filler particles in mechanical test - Google Patents

Abstract

The present invention discloses a method for identifying the contour evolution characteristics of construction waste roadbed filler particles in a mechanical test, comprising the following steps: S1: obtaining a particle arrangement image of the recycled filler; S2: obtaining the shape parameters of each particle in each group of particle files; S3: respectively obtaining the fractal dimension, abundance, circularity, shape coefficient and crushing rate of each particle in each group of particle files after the mechanical test; S4: calculating the average value of the shape parameters of each particle before and after the mechanical test, and obtaining the shape contour evolution of different particles in each group of particle files; S5: calculating the correlation between different indicators and the crushing rate, and taking the parameter corresponding to the maximum correlation value as the factor with the greatest influence on the crushing of the recycled filler. The present invention can effectively analyze the contour evolution characteristics of different components in the recycled filler before and after the mechanical test, can deeply reveal the crushing mechanism of different components, and can promote the recognition of the road performance of construction waste fillers.

Description

Method for identifying particle profile evolution characteristics of construction waste roadbed filler in mechanical tests

Technical Field

The invention belongs to the technical field of profile evolution, and in particular relates to a method for identifying profile evolution characteristics of building waste roadbed filler particles in a mechanical test.

Background technique

The problem with using construction waste for roadbed filling is that it contains many weak components such as bricks, which are prone to secondary crushing during construction and operation, causing large settlement deformation. Therefore, it is extremely necessary to study the secondary crushing characteristics of each component in the recycled filler. The commonly used method is to screen the recycled filler before and after the mechanical test, which can only obtain the change trend of the filler gradation, and cannot specifically analyze which characteristic parameters of the particles are easier to crush and to what extent. For example, in the article "Fractal Law of Crushing of Recycled Concrete Brick Mixed Aggregate and Determination Method of Aggregate Grading for Pavement Base or Subbase", Xu Pengcheng screened and manually sorted the recycled mixed aggregates before and after crushing, and explored a series of changes in the mass and volume of the recycled mixed aggregates before and after crushing. The existing methods can only obtain the change trend of the mass and volume of fillers of different particle sizes, and cannot conduct detailed quantitative analysis and detailed description of the specific crushing behavior and shape profile evolution of each particle size filler.

Summary of the invention

In order to solve the above problems, the present invention proposes a method for identifying the evolution characteristics of the particle profile of construction waste roadbed filler in a mechanical test.

The technical solution of the present invention is: a method for identifying the particle profile evolution characteristics of construction waste roadbed filler in a mechanical test comprises the following steps:

S1: Screening the recycled filler to obtain a particle arrangement image of the recycled filler;

S2: According to the particle arrangement image of the recycled filler, the shape parameters of each particle in each particle group are obtained;

S3: Conduct mechanical experiments on the recycled filler to obtain the fractal dimension, abundance, circularity, shape coefficient and breakage rate of each particle in each particle group after the mechanical experiment;

S4: Calculate the average value of the shape parameters of each particle before and after the mechanical experiment to obtain the shape profile evolution of different particles in each group of particles;

S5: Based on the change sequence of crushing rate, fractal dimension, abundance, circularity and shape coefficient, the correlation between different indicators and crushing rate is calculated, and the parameter corresponding to the maximum correlation value is taken as the factor with the greatest influence on the crushing of the recycled filler. The shape contour evolution of different particles in each group of particle files and the factor with the greatest influence on the crushing of the recycled filler are taken as the shape contour evolution results.

Furthermore, in step S1, the specific method for collecting the particle arrangement image is: screening the recycled filler with different particle sizes to obtain each group of particle files; dividing the recycled filler of each group of particle files into four parts, and randomly sampling from each group of particle files; using a camera to collect sample images of each group of particle files to obtain a particle arrangement image.

Further, in step S2, the particles include bricks, concrete blocks and stones;

The shape parameters include particle abundance C, circularity R and shape coefficient F, and their calculation formulas are:

Among them, 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 circumscribed circle of the particle, P represents the circumference of a circle with the same area as the particle, and S represents the actual circumference of the particle.

Furthermore, in step S3, the mechanical experiment causes the particles to break, so that the shape profile of each particle changes.

Furthermore, in step S3, the calculation formula of the crushing rate _Bg of each particle size is:

B _g =Σ|ΔW _k |

ΔW _k =W _ki -W _kf

Among them, W _ki represents the content of the particle size on the particle grading curve before the mechanical test, W _kf represents the content of the same particle size on the particle grading curve after the mechanical test, and ΔW _k represents the absolute difference in the particle size content before and after the test.

Furthermore, step S5 includes the following sub-steps:

S51: taking the fragmentation rate of each grain file as the parent sequence, taking the fractal dimension as the first characteristic sequence, taking the abundance as the second characteristic sequence, taking the circularity as the third characteristic sequence, and taking the shape coefficient as the fourth characteristic sequence;

S52: performing dimensionless processing on the parent sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating the correlation coefficient between each characteristic sequence and the parent sequence after dimensionless tempering;

S53: Calculate the average value of the correlation coefficients between each feature sequence and the parent sequence, and use the average value as the correlation degree.

Further, in step S51, the expression of the mother sequence _Xb is _Xb = { _xb (k)|k = 1, 2, ...}, the expression of the first characteristic sequence _Xw is _Xw = { _xw (k)|k = 1, 2, ...}, the expression of the second characteristic sequence X is _Xc = { _xc (k)|k = 1, 2, ...}, the expression of the third characteristic sequence _Xr is _Xr = { _xr (k)|k = 1, 2, ...}, and the expression of the fourth characteristic sequence _Xf is _Xf = { _xf (k)|k = 1, 2, ...}; wherein _xb (k) represents each fragmentation rate value, _xw (k) represents each fractal dimension value, _xc (k) represents each abundance value, _xr (k) represents each circularity value, and _xf (k) represents each shape coefficient value;

In step S52, each sequence is dimensionlessly processed to convert the _Xi sequence into the _Yi sequence, and the calculation formula is:

Among them: _yi (k) represents the data _{of Yi} sequence after dimensionless processing, _xi (k) represents the data of _Xi sequence, _xi (l) represents the average value of a sequence, n represents the number of sequence data;

In step S52, the calculation formula of the correlation coefficient ξ _i (k) between different feature sequences and the mother sequence is:

Wherein, Δ _b,i (k) represents the absolute difference of the two sequences at time k, Δ _max (b,i) and Δ _min (b,i) are the maximum and minimum values of the absolute differences at each time, ρ represents the resolution coefficient, 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 coefficient between each feature sequence and the mother sequence is:

Where n represents the sequence length.

The beneficial effect of the present invention is that: currently, most of the methods of screening the recycled filler before and after the mechanical test can only obtain the gradation change of the recycled filler before and after the mechanical test, but cannot reveal the shape evolution law of different particles before and after the mechanical test, and cannot reveal the crushing behavior of different components. The present invention can effectively analyze the contour evolution characteristics of different components in the recycled filler before and after the mechanical test, can deeply reveal the crushing mechanism of different components, and can promote the understanding of the road performance of construction waste filler.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for identifying the particle profile evolution characteristics of construction waste roadbed filler;

Figure 2 shows images of regenerated filler GS _{20-40 -1, 2} , 3, 4;

FIG3 is a diagram showing the use of an AOI tool to track and trace the outline of a brick;

FIG4 is a process diagram for selecting measurement items and performing measurements;

FIG5 is a diagram showing the evaluation index coefficients for calculating the shape changes of each component in the regenerated filler according to the output measurement results;

Figure 6 is a characteristic diagram of brick abundance distribution before and after the mechanical test.

Detailed ways

The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

As shown in FIG1 , the present invention provides a method for identifying the particle profile evolution characteristics of construction waste roadbed filler in a mechanical test, comprising the following steps:

S1: Screening the recycled filler to obtain a particle arrangement image of the recycled filler;

S2: According to the particle arrangement image of the recycled filler, the shape parameters of each particle in each particle group are obtained;

S3: Conduct mechanical experiments on the recycled filler to obtain the fractal dimension, abundance, circularity, shape coefficient and breakage rate of each particle in each particle group after the mechanical experiment;

S4: Calculate the average value of the shape parameters of each particle before and after the mechanical experiment to obtain the shape profile evolution of different particles in each group of particles;

S5: Based on the change sequence of crushing rate, fractal dimension, abundance, circularity and shape coefficient, the correlation between different indicators and crushing rate is calculated, and the parameter corresponding to the maximum correlation value is taken as the factor with the greatest influence on the crushing of the recycled filler. The shape contour evolution of different particles in each group of particle files and the factor with the greatest influence on the crushing of the recycled filler are taken as the shape contour evolution results.

In an embodiment of the present invention, in step S1, the specific method for collecting the particle arrangement image is: screening the recycled filler with different particle sizes to obtain each group of particle files; dividing the recycled filler of each group of particle files into four parts, and randomly sampling from each group of particle files; using a camera to collect sample images of each group of particle files to obtain a particle arrangement image.

In the embodiment of the present invention, the recycled filler is screened before the mechanical test and different particle sizes are weighed and recorded. The particle size interval of the filler screening can be selected according to the actual material conditions. Here, three particle sizes of 0-10mm, 10-20mm, and 20-40mm are taken as examples. Previous studies have shown that the contour recognition error is large when the particles are too fine, and considering that fine particles are generally more stable, it is recommended to use 10mm as the dividing line, and the contour analysis is performed on particles larger than 10mm.

Random sampling: Divide the filler of each particle group into four equal parts, and randomly sample one part from each equal part, that is, take four parallel samples from each particle group, and record them as GS _10～20 -i and GS _20～40 -i respectively. The sampling process ensures that there are about 100 particles in each sample. In _{GS 10～20} -i, 10～20mm represents the particle size range; i＝1～4; GS is the abbreviation of Grain Size. Taking GS _10～20 -1 as an example, it represents the first sample of the 10～20mm particle size range.

Place a whiteboard with a reference ruler on the table or on the ground, fix the high-definition camera directly above it, and adjust the camera angle. Debug the camera's shooting parameters before shooting to ensure the best shooting effect. The camera position and camera shooting parameters remain unchanged during the shooting of the same grain size filler. Taking GS _10~20-1 as an example, randomly place the filler particles in the sample on the whiteboard in step 3. The arrangement should ensure that the material distribution is random and does not overlap or touch each other. Then manually identify the components such as bricks, cement mortar, concrete blocks, and stones in the sample, and number each particle. Take a photo of each arranged sample to obtain an image of the particle arrangement. Import the photos taken into Image-Pro Plus (IPP) software. Use the ruler on the cardboard to calibrate the measurement system in the software 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 include particle abundance C, circularity R and shape coefficient F, and their calculation formulas are:

Among them, 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 circumscribed circle of the particle, P represents the circumference of a circle with the same area as the particle, and S represents the actual circumference of the particle.

In the embodiment of the present invention, the AOI tool is used to outline each granule, and the measurement object is generated by converting AOI toobject. The shape parameters such as area (A), perimeter (S), major axis size (L), minor axis size (B) of each granule are automatically measured in the system.

In the embodiment of the present invention, in step S3, the mechanical experiment causes the particles to break, so that the shape profile of each particle changes.

In the embodiment of the present invention, in step S3, the calculation formula of the crushing rate _Bg of each particle size is:

B _g =Σ|ΔW _k |

ΔW _k =W _ki -W _kf

Among them, W _ki represents the content of the particle size on the particle grading curve before the mechanical test, W _kf represents the content of the same particle size on the particle grading curve after the mechanical test, and ΔW _k represents the absolute difference in the particle size content before and after the test.

In the embodiment of the present invention, the shape parameters of the particles in each particle size of the mechanical test are counted to obtain the distribution range and average value 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 law of this parameter under the mechanical test. Secondly, the evolution characteristics of the shape contours of the particles of different components in each particle size before and after the mechanical test can also be obtained based on the component characteristics.

Taking the abundance of bricks as an example, it indicates the degree of oblateness of the bricks. The smaller the value, the closer the surface is to needle-like. The closer the value is to 1, the more equal the major and minor axes are. The distribution range of this parameter indicates the distribution of the main degree of oblateness of the bricks in the current state. By comparing the abundance of bricks before and after the mechanical test, it can be reflected that the law of change of the degree of oblateness of the bricks under this test can be reflected. Multiple tests can obtain a relatively fixed value of abundance, that is, at what degree of oblateness the particles of the bricks tend to be stable. The remaining parameters of other components can also obtain their evolution laws.

In this embodiment of the present invention, step S5 includes the following sub-steps:

S51: taking the fragmentation rate of each grain file as the parent sequence, taking the fractal dimension as the first characteristic sequence, taking the abundance as the second characteristic sequence, taking the circularity as the third characteristic sequence, and taking the shape coefficient as the fourth characteristic sequence;

S52: performing dimensionless processing on the parent sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating the correlation coefficient between each characteristic sequence and the parent sequence after dimensionless tempering;

S53: Calculate the average value of the correlation coefficients between each feature sequence and the parent sequence, and use the average value as the correlation degree.

In the embodiment of the present invention, in step S51, the expression of the mother sequence _Xb is _Xb = { _xb (k)|k = 1, 2, ...}, the expression of the first characteristic sequence _Xw is _Xw = { _xw (k)|k = 1, 2, ...}, the expression of the second characteristic sequence X is _Xc = { _xc (k)|k = 1, 2, ...}, the expression of the third characteristic sequence _Xr is _Xr = { _xr (k)|k = 1, 2, ...}, and the expression of the fourth characteristic sequence _Xf is _Xf = { _xf (k)|k = 1, 2, ...}; wherein _xb (k) represents each fragmentation rate value, _xw (k) represents each fractal dimension value, _xc (k) represents each abundance value, _xr (k) represents each circularity value, and _xf (k) represents each shape coefficient value;

In step S52, each sequence is dimensionlessly processed to convert the _Xi sequence into the _Yi sequence, and the calculation formula is:

Among them: _yi (k) represents the data _{of Yi} sequence after dimensionless processing, _xi (k) represents the data of _Xi sequence, _xi (l) represents the average value of a sequence, n represents the number of sequence data;

In step S52, the calculation formula of the correlation coefficient ξ _i (k) between different feature sequences and the mother sequence is:

Wherein, Δ _b,i (k) represents the absolute difference of the two sequences at time k, Δ _max (b,i) and Δ _min (b,i) are the maximum and minimum values of the absolute differences at each time, ρ represents the resolution coefficient, 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 coefficient between each feature sequence and the mother sequence is:

Where n represents the sequence length.

In the embodiment of the present invention, the main indicators of the impact of different components on the fragmentation rate are analyzed. The fractal dimension, abundance, circularity, and shape coefficient represent the fractal degree, oblateness, circularity, and complexity of the contour of the particles, respectively. These indicators will affect whether the particles are easy to break. However, due to differences in composition, the indicators that affect the degree of difficulty of breaking may be different for particles of different components. For different components, the correlation between the calculated characteristic sequence and the parent sequence is sorted, and the indicator with the largest correlation is the factor that is most likely to affect the fragmentation of the component.

The present invention is explained below in conjunction with specific embodiments. The mechanical test conducted in this embodiment is vibration compaction, and the rating indicators for evaluating the shape evolution law of each component of the recycled filler include: abundance, circularity, and shape coefficient. Among them, Figure 2 is an image of recycled filler GS _20~ 40-1, 2, 3, and 4, Figure 3 is a diagram of using the AOI tool to track and depict the outline of the brick, Figure 4 is a process of selecting measurement items and measuring, and the measurement items of this embodiment include Aspect, Axis (major), Axis (minor), Perimeter, and Area (polygon). Figure 5 is an evaluation index coefficient for the shape change of each component in the recycled filler calculated based on the output measurement results, and Figure 6 is the abundance distribution characteristics of bricks 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 different components and the crushing rate. From the analysis results, we can see that: after vibration compaction, the abundance coefficients of stones, bricks and concrete blocks increase significantly, and the particle abundance after compaction is closer to 1; in terms of roundness, both stones and bricks have obvious changes, and particles with smaller roundness are easier to break; and the shape coefficients of stones and bricks also increase significantly after compaction.

Table 1

Table 2

table 3

The beneficial effects of the present invention are as follows: currently, most of the methods of screening the recycled filler before and after the mechanical test can only obtain the gradation change of the recycled filler before and after the mechanical test, but cannot reveal the shape evolution law of different particles before and after the mechanical test, and cannot reveal the crushing behavior of different components. The present invention can effectively analyze the contour evolution characteristics of different components in the recycled filler before and after the mechanical test, can deeply reveal the crushing mechanism of different components, and can promote the understanding of the road performance of construction waste filler.

Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims (7)

1. A method for identifying the particle profile evolution characteristics of construction waste roadbed filler in a mechanical test, characterized by comprising the following steps: S1: Screening the recycled filler to obtain a particle arrangement image of the recycled filler; S2: According to the particle arrangement image of the recycled filler, the shape parameters of each particle in each particle group are obtained; S3: Conduct mechanical experiments on the recycled filler to obtain the fractal dimension, abundance, circularity, shape coefficient and breakage rate of each particle in each particle group after the mechanical experiment; S4: Calculate the average value of the shape parameters of each particle before and after the mechanical experiment to obtain the shape profile evolution of different particles in each group of particles; S5: Based on the change sequence of crushing rate, fractal dimension, abundance, circularity and shape coefficient, the correlation between different indicators and crushing rate is calculated, and the parameter corresponding to the maximum correlation value is taken as the factor with the greatest influence on the crushing of the recycled filler. The shape contour evolution of different particles in each group of particle files and the factor with the greatest influence on the crushing of the recycled filler are taken as the shape contour evolution results. 2. The method for identifying the evolution characteristics of the particle contour of construction waste roadbed filler in a mechanical test according to claim 1 is characterized in that in the step S1, the specific method for collecting the particle arrangement image is: screening the recycled filler with different particle sizes to obtain each group of particle files; dividing the recycled filler of each group of particle files into four parts, and randomly sampling from each group of particle files; using a camera to collect sample images of each group of particle files to obtain a particle arrangement image. 3. The method for identifying the particle profile evolution characteristics of construction waste roadbed filler in mechanical tests according to claim 1, characterized in that in step S2, the particles include bricks, concrete blocks and stones; The shape parameters include particle abundance C, circularity R and shape coefficient F, and their calculation formulas are: Among them, 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 circumscribed circle of the particle, P represents the circumference of a circle with the same area as the particle, and S represents the actual circumference of the particle. 4. The method for identifying the evolution characteristics of the particle contour of construction waste roadbed filler in a mechanical test according to claim 1 is characterized in that in the step S3, the mechanical experiment causes the particles to break, causing the shape contour of each particle to change. 5. The method for identifying the particle profile evolution characteristics of construction waste roadbed filler in mechanical tests according to claim 1, characterized in that in step S3, the calculation formula of the crushing rate _Bg of each particle file is: B _g =∑|ΔW _k | ΔW _k =W _ki -W _kf Among them, W _ki represents the content of the particle size on the particle grading curve before the mechanical test, W _kf represents the content of the same particle size on the particle grading curve after the mechanical test, and ΔW _k represents the absolute difference in the particle size content before and after the test. 6. The method for identifying the particle profile evolution characteristics of construction waste roadbed filler in mechanical tests according to claim 1, characterized in that step S5 comprises the following sub-steps: S51: taking the fragmentation rate of each grain file as the parent sequence, taking the fractal dimension as the first characteristic sequence, taking the abundance as the second characteristic sequence, taking the circularity as the third characteristic sequence, and taking the shape coefficient as the fourth characteristic sequence; S52: performing dimensionless processing on the parent sequence, the first characteristic sequence, the second characteristic sequence, the third characteristic sequence and the fourth characteristic sequence, and calculating the correlation coefficient between each characteristic sequence and the parent sequence after dimensionless tempering; S53: Calculate the average value of the correlation coefficients between each feature sequence and the parent sequence, and use the average value as the correlation degree. 7. The method for identifying the particle profile evolution characteristics of construction waste roadbed filler in mechanical tests according to claim 6, characterized in that, in the step S51, the expression of the parent sequence _Xb is _Xb = { _xb (k) | k = 1, 2, ...}, the expression of the first characteristic sequence _Xw is _Xw = { _xw (k) | k = 1, 2, ...}, the expression of the second characteristic sequence X is Xc = { _xc (k) | k = 1, 2, ...}, the expression of the third characteristic sequence _Xr is _Xr = { _xr (k) | k = 1, 2, ...}, and the expression of the fourth characteristic sequence _Xf is _Xf = { _xf (k) | k = 1, 2, ...}; wherein _xb (k) represents each breakage rate value, _xw (k) represents each fractal dimension value, _xc (k) represents each abundance value, _xr (k) represents each circularity value, and _xf (k) represents each shape coefficient value; In step S52, each sequence is dimensionlessly processed to convert the _Xi sequence into a _Yi sequence, and the calculation formula is: Among them: _yi (k) represents the data _{of Yi} sequence after dimensionless processing, _xi (k) represents the data of _Xi sequence, _xi (l) represents the average value of a sequence, n represents the number of sequence data; In step S52, the calculation formula of the correlation coefficient ξ _i (k) between different feature sequences and the mother 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 and minimum values of the absolute differences at each time, ρ represents the resolution coefficient, 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 coefficient between each feature sequence and the mother sequence is: Where n represents the sequence length.