Aggregate grading analysis method and equipment based on digital screening

Abstract

The invention relates to the field of engineering digital detection, in particular to an aggregate grading analysis method and equipment based on digital screening, and the method comprises the following steps: acquiring three-dimensional surface information of an aggregate to be analyzed, and establishing an aggregate surface three-dimensional model; and de-noising the aggregate surface three-dimensional model to generate an aggregate cluster three-dimensional model. And carrying out segmentation processing on the aggregate cluster three-dimensional model, and outputting a plurality of single aggregate three-dimensional model files. And traversing the plurality of single aggregate three-dimensional model files, and calculating the screening size. And outputting a grading curve according to the sieving size. The problems that the efficiency of a traditional grading screening process is insufficient, and too much dust is generated on the site in the screening process are effectively solved, the method has the advantages in the aspects of precision, efficiency and green production, an innovative technical means is provided for aggregate screening, and therefore support is provided for improving the mix proportion design of concrete and asphalt mixtures; therefore, the performance of the engineering material is improved.

Description

Technical Field

[0001] The present invention relates to the field of engineering digital inspection, and particularly to an aggregate gradation analysis method and device based on digital screening. Background Art

[0002] Aggregates are important components of building materials such as concrete and asphalt, usually made of natural rocks or artificial materials, and are mainly divided into two categories: fine aggregates and coarse aggregates. The particle size and gradation of aggregates have a significant impact on the strength, durability and workability of building materials. Therefore, aggregate gradation analysis is an important link to ensure project quality. Gradation analysis evaluates the distribution characteristics by measuring the proportion of particles with different particle sizes in the aggregate, so as to guide the design and optimization of the mixture.

[0003] Traditional aggregate gradation analysis methods are mainly carried out by physical screening. Its basic process includes sample preparation, classification screening, weighing and data processing, that is, using a set of standard sieves to classify the aggregates, gradually screening the aggregate samples into sieves with different particle sizes through mechanical vibration or manual operation, then weighing the mass of the aggregates remaining on each sieve layer respectively, and calculating the mass percentage of aggregates of each particle size based on these data, and drawing a particle size distribution curve. This method has been widely used in the engineering and construction fields to evaluate and control the quality of aggregates.

[0004] However, the traditional screening method has some obvious disadvantages. First, the operation is cumbersome, requiring a lot of manpower and time, including sample preparation, screening and weighing. The operation process is complex and the labor intensity is high. Second, the aperture and shape of the sieve will affect the screening result, and it is difficult to accurately control the screening process, especially for fine-grained aggregates, errors are likely to occur. In addition, the screening process usually takes a long time, especially when a large number of samples need to be processed, and the efficiency is significantly insufficient. Long-term use of the sieve for screening will cause the sieve to wear, which will in turn affect the screening effect, and the sieve needs to be replaced regularly, increasing the maintenance cost. Due to the limitations of manual operation and equipment, the screening results obtained by different operators or the same operator at different times may vary, and it is difficult to ensure the repeatability and consistency of the results. In addition, certain dust will be generated during the traditional screening process, which has an adverse impact on the health of operators and the working environment.

[0005] Therefore, there is a need for an aggregate gradation analysis method based on digital screening with less manual participation, higher accuracy and higher efficiency. Summary of the Invention

[0006] The purpose of the present invention is to overcome the above-mentioned deficiencies existing in the prior art, and provide an aggregate gradation analysis method and device based on digital screening.

[0007] To achieve the above-mentioned invention object, the present invention provides the following technical solutions:

[0008] An aggregate gradation analysis method based on digital screening, comprising the following steps:

[0009] S1: Obtain the three-dimensional surface information of the aggregate to be analyzed and establish a three-dimensional model of the aggregate surface;

[0010] S2: Denoise the three-dimensional model of the aggregate surface to generate a three-dimensional model of the aggregate cluster;

[0011] S3: Segment the three-dimensional model of the aggregate cluster and output several three-dimensional model files of single aggregates;

[0012] S4: Traverse the several three-dimensional model files of single aggregates and calculate the sieve size;

[0013] S5: Output a gradation curve according to the sieve size.

[0014] As a preferred solution of the present invention, the three-dimensional model of the aggregate surface is composed of several triangular patch files in STL format.

[0015] As a preferred solution of the present invention, the denoising process includes:

[0016] Use digital shape processing software to fill the holes on the aggregate surface in the three-dimensional model of the aggregate surface and delete the patch information that does not belong to the aggregate itself.

[0017] As a preferred solution of the present invention, the segmentation process includes:

[0018] Read the STL file in the three-dimensional model of the aggregate cluster;

[0019] Calculate the side length dimension of the bounding box of each aggregate;

[0020] Set a filtering threshold;

[0021] When the side length dimension of the bounding box is greater than the filtering threshold, extract the three-dimensional model file of a single aggregate until the segmentation of all aggregates is completed.

[0022] As a preferred solution of the present invention, the segmentation process is carried out using a Python program.

[0023] As a preferred solution of the present invention, in S4, the sieve size is calculated by a bounding box algorithm based on simulated annealing.

[0024] As a preferred solution of the present invention, S4 includes the following steps:

[0025] S41: Traverse the several three-dimensional model files of single aggregates and select any one of the three-dimensional model files of single aggregates;

[0026] S42: Rotate the aggregate corresponding to the single aggregate three-dimensional model file around the three-dimensional coordinate axes, search for the volume of the minimum circumscribed cuboid of the aggregate through an optimization algorithm, and use the long side direction of the minimum circumscribed cuboid as the main axis direction of the aggregate;

[0027] S43: Equally divide the aggregate at equal intervals along the main axis of the aggregate, and connect the centers of gravity of each equal part to generate a search path; the cutting spacing of the equal interval division is a preset value;

[0028] S44: Make the aggregate cross-section on the search path perpendicular to the main axis, and obtain the aggregate cross-section with the largest long side of the circumscribed rectangle among each aggregate cross-section as the control plane;

[0029] S45: Rotate the control plane so that the difference between the long side and the short side of the circumscribed rectangle of the plane is less than the threshold; and output the size of the circumscribed rectangle of the current plane as the sieve-passing size corresponding to the current aggregate;

[0030] S46: Enter S41 until the sieve-passing size calculation of all the single aggregate three-dimensional model files is completed.

[0031] As a preferred solution of the present invention, the abscissa of the grading curve is the sieve size, and the ordinate is the passing percentage; the passing percentage = the mass of the aggregate with a sieve-passing size smaller than the sieve size ÷ the total mass of the aggregate.

[0032] An aggregate grading analysis device based on digital screening includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of the above.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] The present invention provides an aggregate grading analysis method and device based on digital screening, including: obtaining the three-dimensional surface information of the aggregate to be analyzed and establishing a three-dimensional model of the aggregate surface. Denoising the three-dimensional model of the aggregate surface to generate a three-dimensional model of the aggregate cluster. Performing segmentation processing on the three-dimensional model of the aggregate cluster and outputting a number of single aggregate three-dimensional model files. Traversing the number of single aggregate three-dimensional model files and calculating the sieve-passing size. Outputting a grading curve according to the sieve-passing size. The present invention effectively solves the problems of insufficient efficiency in the traditional grading screening process and excessive dust generated on-site during the screening process, has advantages in terms of accuracy, efficiency and green production, provides an innovative technical means for aggregate screening, thereby providing support for improving the mix design of concrete and asphalt mixtures, and thus improving the performance of engineering materials. Description of the Drawings

[0035] Figure 1 This is the overall flowchart of a digital screening-based aggregate gradation analysis method described in Embodiment 1 of the present invention.

[0036] Figure 2 This is a schematic diagram of the three-dimensional model of the scanned aggregate surface in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention.

[0037] Figure 3 This is in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention Figure 2 Schematic diagram of the three-dimensional model of the denoised aggregate cluster.

[0038] Figure 4 This is in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention Figure 3 Schematic diagram of the three-dimensional model of a single automatically segmented aggregate.

[0039] Figure 5 This is the flowchart of the bounding box algorithm in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention.

[0040] Figure 6 This is a schematic diagram of the digital screening algorithm of aggregates in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention simulating the traditional vibrating screen process.

[0041] Figure 7 This is a comparison diagram of gradation curves drawn according to the first set of aggregate data in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention.

[0042] Figure 8 This is a comparison diagram of gradation curves drawn according to the second set of aggregate data in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention.

[0043] Figure 9 This is a comparison diagram of gradation curves drawn according to the third set of aggregate data in a digital screening-based aggregate gradation analysis method described in Embodiment 2 of the present invention.

[0044] Figure 10 This is a schematic diagram of the structure of a digital screening-based aggregate gradation analysis device that utilizes the digital screening-based aggregate gradation analysis method described in Embodiment 1 in Embodiment 3 of the present invention. Detailed Description of the Invention

[0045] The present invention will be further described in detail below in combination with test examples and specific embodiments. However, it should not be understood that the scope of the above-mentioned subject matter of the present invention is limited to the following embodiments. Any technology implemented based on the content of the present invention belongs to the scope of the present invention.

[0046] Embodiment 1

[0047] As Figure 1 shown, a method for analyzing the aggregate gradation based on digital screening includes the following steps:

[0048] S1: Obtain the three-dimensional surface information of the aggregate to be analyzed and establish a three-dimensional model of the aggregate surface.

[0049] S2: Denoise the three-dimensional model of the aggregate surface to generate a three-dimensional model of the aggregate cluster.

[0050] S3: Segment the three-dimensional model of the aggregate cluster and output several three-dimensional model files of single aggregates.

[0051] S4: Traverse the several three-dimensional model files of single aggregates and calculate the sieve size.

[0052] S5: Output the gradation curve according to the sieve size.

[0053] In this embodiment, a digital screening technology is established. By collecting and analyzing the three-dimensional model of the aggregate, the particle size distribution information of the aggregate can be obtained quickly and accurately. Compared with the traditional method, digital screening not only greatly saves costs, but also has the advantages of simple operation, high precision, high efficiency and environmental friendliness. It is a more advanced and practical method for analyzing the aggregate gradation.

[0054] Embodiment 2

[0055] This embodiment is a specific implementation manner of the method for analyzing the aggregate gradation based on digital screening described in Embodiment 1, and includes the following steps:

[0056] S1: Obtain the three-dimensional surface information of the aggregate to be analyzed and establish a three-dimensional model of the aggregate surface.

[0057] The three-dimensional surface information is obtained by scanning the surface of the aggregate to be analyzed with a three-dimensional scanner (the scanning distance is not greater than 30 cm). The three-dimensional model of the aggregate surface is composed of several triangular patch files in STL format. Among them, each STL format file stores the three-dimensional model of a single aggregate.

[0058] The STL file is a common 3D model file format used to represent the surface geometry of three-dimensional objects. The STL file consists of a series of connected triangles (usually called patches), and each patch is defined by three vertices and a normal vector.

[0059] Furthermore, before collecting the aggregates to be analyzed, preprocessing at the physical level is also involved. Specifically, it includes operations such as washing and weighing the aggregates.

[0060] S2: Denoise the three-dimensional model of the aggregate surface to generate a three-dimensional model of the aggregate cluster.

[0061] The denoising process includes:

[0062] Using digital shape processing software (such as Geomagic software) to fill the holes on the aggregate surface in the three-dimensional model of the aggregate surface, and deleting the patch information that does not belong to the aggregate itself, to obtain the denoised three-dimensional model of the aggregate cluster, as Figure 2 and Figure 3 shown.

[0063] S3: Segment the three-dimensional model of the aggregate cluster and output several three-dimensional model files of single aggregates.

[0064] The segmentation process uses a Python program to batch process the aggregate cluster to achieve automated segmentation.

[0065] The segmentation process is carried out using a Python program and includes:

[0066] Read the STL file in the three-dimensional model of the aggregate cluster;

[0067] Calculate the side length dimension of the bounding box of each aggregate;

[0068] Set the filtering threshold;

[0069] When the side length dimension of the bounding box is greater than the filtering threshold, extract the three-dimensional model file of a single aggregate, as Figure 4 shown, until the segmentation of all aggregates is completed.

[0070] The aggregate model data is divided into three groups. The first group has 1645 aggregates, the second group has 1728 aggregates, and the third group has 1665 aggregates, with a total of 5038 aggregates. The distribution of each group of data in different particle size ranges is shown in Table 1 below.

[0071] Table 1 Statistical table of aggregates in each particle size of each group of data (unit: piece)

[0072] Particle size The first group The second group The third group 1 (37.5mm - 53mm) 40 40 40 2 (31.5mm - 37.5mm) 80 80 84 3 (26.5mm - 31.5mm) 100 105 100 4 (19mm - 26.5mm) 399 404 400 5 (16mm - 19mm) 298 345 300 6 (9.5mm - 16mm) 440 440 441 7 (4.75mm - 9.5mm) 288 314 300

[0073] S4: Traverse the several three-dimensional model files of single aggregates and calculate the sieve size.

[0074] The sieving size is calculated by a bounding box algorithm based on simulated annealing. The "Danish Box" minimum bounding box algorithm is a method for measuring the triaxial dimensions of aggregates based on experimental means. The basic idea of the measurement is to place the aggregate in a box with movable boundaries, adjust the orientation of the aggregate, and shrink the boundaries of the box in real time to obtain the orientation of the aggregate when the volume of the box is minimized, and use the length, width, and height of the box as the long, medium, and short axis dimensions of the aggregate. Essentially, this method is a triaxial measurement method for aggregates by finding the minimum volume bounding box.

[0075] Further, as Figure 5 shown, it includes the following steps:

[0076] S41: Traverse the three-dimensional model files of the several single aggregates, and select any three-dimensional model file of a single aggregate;

[0077] S42: Rotate the aggregate corresponding to the three-dimensional model file of the single aggregate around the three-dimensional coordinate axes, search for the volume of the minimum circumscribed cuboid of the aggregate through an optimization algorithm (in this embodiment, the simulated annealing algorithm is used), and use the long side direction of the minimum circumscribed cuboid as the main axis direction of the aggregate. Further, the optimization algorithm can also adopt one or more of simulated annealing, genetic algorithm, neural network, beetle antennae search algorithm, sparrow search algorithm, and dung beetle optimization algorithm.

[0078] S43: Cut the aggregate equidistantly into several equal parts along the main axis of the aggregate, and connect the centers of gravity of each equal part to generate a search path; the cutting spacing of the equidistant cutting is a preset value.

[0079] Whether an aggregate can pass through the sieve hole is determined by the control plane inside the aggregate. Therefore, the key to solving the minimum sieving size of the aggregate model lies in the search for the control plane. The complexity of the aggregate shape determines that the aggregate does not pass through the sieve hole strictly along the main axis direction. It is necessary to construct a control plane search path on the basis of fully considering the variation characteristics of the aggregate shape along the main axis direction. In this embodiment, the solid model of the aggregate is divided into equal-height parts along the main axis, and the centers of gravity of each cross-section are connected to obtain several line segments. The connected line segments form a broken line, which is called the search path, as Figure 6 shown.

[0080] S44: Perpendicular to the main axis, obtain the cross-section of the aggregate on the search path, and take the cross-section of the aggregate with the largest long side of the circumscribed rectangle among all the cross-sections of the aggregate as the control plane.

[0081] S45: Rotate the control plane so that the difference between the long side and the short side of the circumscribed rectangle of the plane is less than the threshold; and output the size of the current circumscribed rectangle of the plane as the sieving size corresponding to the current aggregate.

[0082] Map the control plane to two dimensions, keeping its shape unchanged. Rotate it in one direction until the length and width of its bounding box are approximately equal. Take the minimum value of the length and width, which is the minimum sieve size of the aggregate.

[0083] S46: Enter S41 until the sieve size calculation for all the single aggregate 3D model files is completed.

[0084] S5: Output the grading curve according to the sieve size.

[0085] The abscissa of the grading curve is the sieve particle size, and the ordinate is the passing percentage; the passing percentage = the mass of the aggregate with a sieve size smaller than the sieve particle size ÷ the total mass of the aggregate.

[0086] In grading analysis, data is usually plotted as a curve, i.e., the grading curve. The abscissa represents the particle size, and the ordinate is the passing percentage. The passing percentage is used to judge the percentage of the mass of the aggregate passing through a certain sieve in the total mass, and numerically it is equal to 100 minus the cumulative sieve residue percentage of the sieve aperture.

[0087] The particle size is used to judge whether the aggregate can pass through the current sieve, thereby screening the aggregate into different grading ranges. The minimum bounding box algorithm can be used to obtain the sieve size of the aggregate as the basis for aggregate particle size division.

[0088] Since digital screening cannot directly obtain the aggregate mass from the aggregate STL data, the MATLAB software is used to calculate the volume of each aggregate, and instead of the mass, the cumulative sieve residue, cumulative sieve residue percentage, and passing percentage of each grading are calculated, and then the passing percentage is calculated.

[0089] On the other hand, according to Archimedes' principle, the buoyancy force on a stationary object in a liquid is equal to the weight of the liquid displaced by the object. By using a floating scale balance, the mass of each aggregate in air and water environments is measured, and the volume of each aggregate is converted according to the mass difference. This method is a manual measurement method and can be used as a comparison benchmark for digital screening and traditional screening methods.

[0090] The calculation formula for the manual measurement method is as follows:

[0091]

[0092] Where, V agg is the aggregate volume, m a and m w are the masses of the aggregate in air and water respectively, g is the acceleration due to gravity, and ρ is the density of water. The floating scale balance can measure the masses of the aggregate in air and water media respectively, and thus, the volume data of each aggregate is obtained.

[0093] By calculating the three sets of data in the statistical table of each particle size aggregate for each group of data, the grading curves of digital screening, traditional screening, and manual screening are obtained as shown respectively in Figure 7 , Figure 8 , Figure 9 .

[0094] Among them, the solid black line with circular marks in the figure is calculated by the digital screening method, that is, the grading curve calculated by digital screening based on the three-dimensional model of the aggregate; the black dashed line is calculated by the traditional screening method, that is, the grading curve obtained by classifying the aggregate particle sizes using an aggregate vibrating sieve and weighing; the black dashed line with cross marks is calculated by manual screening, that is, the grading curve obtained by converting the physical volume of the aggregate according to the buoyancy difference and treating it as mass.

[0095] To quantitatively analyze the error, for the above three sets of data, calculate the error of the grading curve at each particle size of different types. The following Table 2 shows the differences in percentage passing rates between the digital screening grading curve and the traditional screening, and between the digital screening grading curve and the manual screening grading curve respectively.

[0096] Table 2 Difference Table of Percentage Passing Rates of Digital Screening Grading Data for Each Group

[0097]

[0098]

[0099] From the quantitative analysis of the error, it can be concluded that compared with the traditional screening method, the difference in percentage passing rates at each particle size of the digital screening method is within plus or minus 5%, indicating the high efficiency and high precision of the digital method. Manual screening is used as the comparison benchmark for the digital screening method, and the difference in percentage passing rates at each particle size is within plus or minus 5%, indicating the effectiveness of the digital screening method.

[0100] Example 3

[0101] As Figure 10 shown, an aggregate grading analysis device based on digital screening includes at least one processor, a memory communicatively connected to the at least one processor, and at least one input / output interface communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an aggregate grading analysis method based on digital screening as described in the foregoing embodiments. The input / output interface may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data.

[0102] Those skilled in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0103] When the above integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: various media such as removable storage devices, ROM, magnetic disks, or optical discs that can store program codes.

[0104] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An aggregate gradation analysis method based on digital screening, characterized in that Including the following steps: S1: Obtain the three-dimensional surface information of the aggregate to be analyzed and establish a three-dimensional model of the aggregate surface; S2: Denoise the three-dimensional model of the aggregate surface to generate a three-dimensional model of the aggregate cluster; S3: Segment the three-dimensional model of the aggregate cluster and output several three-dimensional model files of single aggregates; S4: Traverse the several three-dimensional model files of single aggregates and calculate the sieve size; S5: Output the grading curve according to the sieve size.

2. The aggregate gradation analysis method based on digital screening according to claim 1, wherein The three-dimensional model of the aggregate surface is composed of several triangular patch files in STL format.

3. The aggregate gradation analysis method based on digital screening according to claim 2, wherein The denoising process includes: Using digital shape processing software to fill the holes on the aggregate surface in the three-dimensional model of the aggregate surface and delete the patch information that does not belong to the aggregate itself.

4. The aggregate gradation analysis method based on digital screening according to claim 2, characterized in that, The segmentation process includes: Read the STL file in the three-dimensional model of the aggregate cluster; Calculate the side length size of the bounding box of each aggregate; Set the filtering threshold; When the side length size of the bounding box is greater than the filtering threshold, extract the three-dimensional model file of a single aggregate until the segmentation of all aggregates is completed.

5. A method for analyzing aggregate gradation based on digital screening according to claim 4, characterized in that The segmentation process is carried out using a Python program.

6. The aggregate gradation analysis method based on digital screening according to claim 2, wherein In S4, the sieve size is calculated by a bounding box algorithm based on simulated annealing.

7. The aggregate gradation analysis method based on digital screening according to claim 6, characterized in that, S4 includes the following steps: S41: Traverse the several three-dimensional model files of single aggregates and select any one of the three-dimensional model files of single aggregates; S42: Rotate the aggregate corresponding to the three-dimensional model file of the single aggregate around the three-dimensional coordinate axes, search for the volume of the minimum circumscribed cuboid of the aggregate through an optimization algorithm, and use the long side direction of the minimum circumscribed cuboid as the main axis direction of the aggregate; S43: Equally divide the aggregate along the main axis of the aggregate into several equal parts, and connect the centers of gravity of each equal part to generate a search path; the cutting spacing of the equal division is a preset value; S44: Make the cross-section of the aggregate on the search path perpendicular to the main axis, and obtain the aggregate cross-section with the largest long side of the circumscribed rectangle in each aggregate cross-section as the control plane; S45: Rotate the control plane so that the difference between the long side and the short side of the circumscribed rectangle of the plane is less than the threshold; and output the size of the current plane circumscribed rectangle as the sieve size corresponding to the current aggregate; S46: Enter S41 until the sieve size calculation of all the three-dimensional model files of single aggregates is completed.

8. The aggregate gradation analysis method based on digital screening according to claim 2, characterized in that The abscissa of the grading curve is the sieve diameter size, and the ordinate is the passing percentage; the passing percentage = the mass of the aggregate with a sieve size smaller than the sieve diameter size ÷ the total mass of the aggregate.

9. An aggregate gradation analysis device based on digital screening, characterized in that, Including at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 8.

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