Evaluation method of fiber asphalt mixture skeleton characteristic parameters based on digital technology

Abstract

This invention discloses a digital technology-based method for evaluating fiber asphalt mixture skeleton characteristic parameters. The method comprises the following steps: obtaining the volume ratio of coarse and fine aggregates in a fiber asphalt mixture; constructing irregular polygonal particles and, based on discrete element technology, constructing multiple "rigid cluster" templates for the coarse aggregates of the asphalt mixture. Using these templates, a two-dimensional discrete element microscopic model of the fiber asphalt mixture aggregate gradation is generated based on the true contours of the "rigid cluster" templates; performing image processing on the two-dimensional discrete element microscopic model to extract the skeleton characteristic parameters of the asphalt mixture; calculating the length distribution frequency of the skeleton characteristic parameters and fitting the optimal length and optimal combination of fibers in the chopped fiber asphalt mixture. By extracting characteristic parameters, the method can guide the optimization design of fiber length combinations in asphalt mixtures from a microscopic perspective. By constructing a two-dimensional discrete element model of the asphalt mixture, the complex process of macroscopic testing is avoided, resulting in a more accurate model.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to a method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology. Background Art

[0002] With the rapid increase in traffic load and volume, the performance requirements for asphalt pavements are becoming increasingly higher. Under the combined effects of the natural environment and vehicle loads, asphalt pavements will develop rutting, cracking, potholes, low-temperature cracking and other defects after 3-5 years of use. Adding fibers as admixtures to asphalt mixtures is relatively simple and has become a common method for improving the performance of asphalt mixtures. The fibers in fiber-asphalt mixtures play a reinforcing, adsorbing and stabilizing role, so that the stress acting on the asphalt mixture is transmitted to the interior of the mixture along the three-dimensional network formed by the fiber-asphalt aggregate, thereby effectively improving the tensile properties and low-temperature cracking performance. In addition, the fiber itself has a relatively large specific surface area, which can effectively reduce the oiliness of asphalt pavement and improve the high-temperature stability of the pavement. Currently, there are many methods that can intuitively display the skeleton pore structure and related parameters of asphalt mixtures. CT scanning technology is used to characterize the pore structure, crack distribution, and microfluid characteristics of asphalt mixtures from a three-dimensional perspective. Based on discrete element technology, an asphalt mixture void model can be established to obtain the characteristic parameters of the mixture skeleton gap. By applying the INP file program to the Abaqus finite element software, the microstructure of the asphalt mixture sample can be reconstructed, thereby calculating the characteristic parameters of the mixture skeleton gap.

[0003] However, traditional macroscale research and design methods focus on the overall macroscopic performance of the mixture, but ignore the analysis of the impact of individual material components on the mixture's performance. Existing technologies focus on image processing of the asphalt mixture skeleton structure, but lack relevant skeleton characteristic parameters that can effectively fit chopped fibers. Therefore, how to extract the gap characteristic parameters of the fiber asphalt mixture skeleton and fit them to the chopped fibers is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a fiber asphalt mixture skeleton characteristic parameter evaluation method based on digital technology to solve the problem that the analysis of the influence of individual material components on the mixture performance is currently ignored and it is difficult to effectively fit the mixture skeleton characteristic parameters with the chopped fibers.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] According to the fiber asphalt mixture ratio results, the volume ratio of coarse and fine aggregates in the fiber asphalt mixture is obtained;

[0009] Irregular polygonal particles are constructed, and multiple "rigid cluster" templates of asphalt mixture coarse aggregate are constructed based on discrete element technology. The "rigid cluster" templates are used to generate a two-dimensional discrete element mesoscopic model of fiber asphalt mixture aggregate gradation under the real contour.

[0010] Performing image processing on a two-dimensional discrete element microscopic model of asphalt mixture aggregate gradation to extract skeleton characteristic parameters of the asphalt mixture, including the longest axis of the aggregate gap, the shortest axis of the aggregate gap, and the average axis length of the aggregate gap;

[0011] The length distribution frequency of the skeleton characteristic parameters is calculated to fit the optimal length and optimal combination of fibers in the chopped fiber asphalt mixture.

[0012] As a preferred solution of the method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology described in the present invention, wherein: the discrete element technology is used to construct multiple asphalt mixture coarse aggregate "rigid cluster" templates, including:

[0013] First, the true contour of the coarse aggregate of the asphalt mixture is fitted by the "cluster" particles, specifically including:

[0014] The "rigid cluster" template of asphalt mixture coarse aggregate is formed by superimposing "cluster" units with different poles and polar axes.

[0015] As a preferred solution of the method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology described in the present invention, the number of "cluster" units is greater than 10, and the size range of the "cluster" units is 1mm to 3mm.

[0016] As a preferred embodiment of the method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology of the present invention, wherein: the shape of the coarse aggregate of the asphalt mixture is similar to an irregular polygon with 4 to 10 sides;

[0017] In a two-dimensional polar coordinate system, the shape parameters of the polygon include the center of the polygon (X i , Y i), the number of sides n of the polygon, the polar radius ri of the i-th side of the polygon, and the polar angle θ of the i-th side of the polygon r .

[0018] As a preferred solution of the method for evaluating the characteristic parameters of fiber asphalt mixture skeleton based on digital technology of the present invention, the shape and size parameters of the polygon are determined by the random number N i control;

[0019] Among them, X max , X min , Y max , Y min are the maximum abscissa, minimum abscissa, maximum ordinate and minimum ordinate of the polygonal two-dimensional section;

[0020] As a preferred solution of the method for evaluating the characteristic parameters of fiber asphalt mixture skeleton based on digital technology of the present invention, wherein: the polygonal particle size is [D s ,D s+1 ];

[0021] X coordinate of polygon center i =X min +N i ×(X max -X min );

[0022] Y coordinate of the polygon center i =Y min +N i ×(Y max -Y min );

[0023] Number of polygon sides n = 4 + N i ×(10-4);

[0024] Polar diameter Polar angle

[0025] As a preferred solution of the fiber asphalt mixture skeleton characteristic parameter evaluation method based on digital technology described in the present invention, before generating a two-dimensional discrete element microscopic model of fiber asphalt mixture aggregate gradation under a real contour through the "rigid cluster" template, it includes:

[0026] Within the range of the specimen mold, various circular coarse aggregate and fine aggregate units are generated according to the aggregate gradation, and the "rigid cluster" contour template library program is loaded for each circular coarse aggregate.

[0027] As a preferred solution of the method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology described in the present invention, the coarse aggregate is characterized by rigid cluster particles, and the fine aggregate particles are characterized by spherical unit particles.

[0028] As a preferred solution of the fiber asphalt mixture skeleton characteristic parameter evaluation method based on digital technology described in the present invention, the skeleton characteristic parameters are obtained from the mineral gap distribution map of the asphalt mixture, and the asphalt mixture mineral gap distribution map is defined as an irregular figure composed of the contact gaps of the aggregate skeleton in the asphalt mixture.

[0029] As a preferred embodiment of the method for evaluating the characteristic parameters of the fiber asphalt mixture skeleton based on digital technology described in the present invention, wherein: the number of the longest axis of the mineral gap, the shortest axis of the mineral gap and the average axis of the mineral gap in the interval (0, 1), [1, 2), [2, 3), ..., [n, +∞) are respectively denoted as X1, X2, ...X i , i is an integer from 1 to n.

[0030] Compared with the existing technology, the present invention has the following beneficial effects: by extracting characteristic parameters, the present invention can guide the optimal design of the length combination of fibers in asphalt mixture from a microscopic perspective; by constructing a two-dimensional discrete element model of asphalt mixture, compared with traditional methods, the operation is simple, the complicated process of macroscopic experiments is avoided, and the resulting model is more intuitive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0032] Figure 1 This is a schematic diagram of the overall process of a method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology according to an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of a discrete element model of EA-10 epoxy asphalt mixture in a method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the discrete element model of AC-13 asphalt mixture in the method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0038] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0039] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0040] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0041] Example 1

[0042] Reference Figure 1, as one embodiment of the present invention, provides a method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology, comprising:

[0043] S1: Based on the fiber asphalt mixture ratio results, obtain the volume ratio of coarse and fine aggregates in the fiber asphalt mixture;

[0044] Preferably, in step S1, the coarse aggregate is an aggregate having a particle size greater than 2.36 mm and smaller than the maximum particle size specified by the grading; the fine aggregate is an aggregate having a particle size less than 2.36 mm.

[0045] Preferably, in step S1, the particle size of each coarse aggregate is D1, D2, D3, ..., D n , 2.36mm, the passing rate of each grade of coarse aggregate is 100%, P D2 , P D3 ,…,P Dn , P 2.36 , then the proportion of coarse aggregate in each grade is 100%-P D2 , P D2 -P D3 , P D3 -P D4 ,…,P Dn-1 -P Dn ,P Dn -P 2.36 The particle size of each grade of fine aggregate is 1.18mm, 0.6mm, 0.3mm, 0.15mm, 0.075mm, and the passing rate of each grade of fine aggregate is P 1.18 , P 0.6 , P 0.3 , P 0.15 , P 0.075 , then the proportion of fine aggregate in each grade is P 2.36 -P 1.18 , P 1.18 -P 0.6 , P 0.6 -P 0.3 , P 0.3 -P 0.15 , P 0.15 -P 0.075 , P 0.075 The proportion of fine aggregate in each grade is P 2.36 , the proportion of coarse aggregate in each grade is 1-P 2.36 .

[0046] Furthermore, the volume ratio of coarse and fine aggregates in the fiber asphalt mixture is obtained, which is expressed as:

[0047]

[0048] The proportion of coarse aggregate to the total volume of the specimen is (1-VMA)×(1-P2.36 );

[0049] The proportion of fine aggregate to the total volume of the specimen is (1-VMA)×P 2.36 ;

[0050] Wherein, VMA represents the void ratio of the mixed material specimen; ρ a is the density of asphalt, ρ b is the density of coarse and fine aggregates, V is the total volume of the asphalt mixture specimen, VV is the designed void ratio of the specimen, v is the volume of coarse and fine aggregates, and a is the oil-stone ratio.

[0051] S2: Irregular polygonal particles are constructed, and multiple "clump" templates of asphalt mixture coarse aggregate are constructed based on discrete element technology. The "clump" templates are used to generate a two-dimensional discrete element microscopic model of the fiber asphalt mixture aggregate gradation under the actual contour.

[0052] Furthermore, before generating the 2D discrete element mesoscopic model of aggregate gradation of fiber asphalt mixture under the real contour through the "rigid cluster" template, including,

[0053] Within the range of the specimen mold, various circular coarse aggregate and fine aggregate units are generated according to the aggregate gradation, and the "rigid cluster" contour template library program is loaded for each circular coarse aggregate.

[0054] It should be noted that MATLAB can be used to construct irregular polygonal particles, and PFC 2D can be used to construct the clump template of asphalt mixture coarse aggregate. In the discrete element software, coarse aggregate particles are represented by clump particles, and fine aggregate particles are represented by spherical unit particles.

[0055] Specifically, the PFC 2D is used to construct a two-dimensional discrete element model of asphalt mixture. The process of constructing a two-dimensional discrete element cross section of asphalt mixture based on the existing coarse aggregate clump template is as follows:

[0056] (1) Define the calculation area and generate the wall: Define the calculation area and generate the wall at the area boundary. To ensure that the aggregate particles are randomly distributed within the wall, the calculation area size is set to two to three times the size of the asphalt mixture specimen. To ensure the accuracy of the characteristic parameters, the final width of the mixture discrete element cross-section wall should be greater than five times the maximum particle size.

[0057] Furthermore, the coarse aggregate is characterized by rigid cluster particles, and the fine aggregate particles are characterized by spherical unit particles.

[0058] (2) Generate coarse aggregate clump units: Read the mixture coarse aggregate clump template, adjust the size of the mixture coarse aggregate clump unit according to the particle size of each coarse aggregate, calculate the volume proportion of each coarse aggregate in the mixture according to the proportion of each coarse aggregate in the total volume of coarse aggregate, and run the command flow to generate each coarse aggregate clump unit.

[0059] (3) Generation of fine aggregate ball units: Based on the proportion of each grade of fine aggregate in the total volume of fine aggregate, the volume proportion of each grade of fine aggregate in the asphalt mixture is calculated, and the command flow is run to generate the fine aggregate ball units of each grade.

[0060] (4) Gravity drop and wall compaction: From step (1), the rectangular specimen is expanded to twice its original height to allow for adequate distribution of the coarse and fine aggregate particle groups. After the coarse and fine aggregate particle groups are generated, the specimen is compacted by the top wall, controlling the distance between the aggregate particles so that they are connected and do not overlap, until the top wall is lowered to the specified specimen height.

[0061] It should be noted that the use of PFC 2D to construct a two-dimensional discrete element model of asphalt mixture is simpler to operate than the traditional method, avoids the complicated process of macroscopic experiments, and the resulting model is more intuitive and accurate.

[0062] Furthermore, multiple asphalt mixture coarse aggregate "rigid cluster" (clump) templates are constructed based on discrete element technology, including:

[0063] First, the true contour of the coarse aggregate of the asphalt mixture is fitted by the "cluster" particles, specifically including:

[0064] The "rigid cluster" template of asphalt mixture coarse aggregate is formed by superimposing "cluster" units with different poles and polar axes.

[0065] Furthermore, the number of "pebbles" units is greater than 10, and the size of "pebbles" units ranges from 1 mm to 3 mm.

[0066] It should be noted that the purpose of the above numerical settings is to make the generated polygonal coarse aggregate "rigid cluster" unit closer to the two-dimensional cross-section of coarse aggregate in real asphalt mixture.

[0067] Furthermore, since the profile of the asphalt mixture coarse aggregate is fitted according to the profile parameters of each grade of particles with a particle size greater than 2.36 mm, the shape of the asphalt mixture coarse aggregate is approximately an irregular polygon with 4 to 10 sides;

[0068] In a two-dimensional polar coordinate system, the shape parameters of a polygon include the center of the polygon (X i , Y i), the number of sides n of the polygon, the polar radius ri of the i-th side of the polygon, and the polar angle θ of the i-th side of the polygon r .

[0069] It should be noted that the particle size is the length of the short side of the minimum circumscribed rectangle.

[0070] Furthermore, the shape and size parameters of the polygon are determined by the random number N i control;

[0071] Among them, X max , X min , Y max , Y min are the maximum abscissa, minimum abscissa, maximum ordinate and minimum ordinate of the polygonal two-dimensional section;

[0072] The polygonal particle size is [D s ,D s+1 ];

[0073] X coordinate of polygon center i =X min +n i ×(X max -X min ) i ;

[0074] Y coordinate of the polygon center i =Y min +N i ×(Y max -Y min );

[0075] Number of polygon sides n = 4 + N i ×(10-4);

[0076] Polar diameter Polar angle

[0077] S3: Perform image processing on the two-dimensional discrete element microscopic model of asphalt mixture aggregate gradation to extract the skeleton characteristic parameters of the asphalt mixture, including the longest axis of the aggregate gap, the shortest axis of the aggregate gap, and the average axis length of the aggregate gap;

[0078] Furthermore, the skeleton characteristic parameters are obtained from the aggregate gap distribution map of the asphalt mixture, which is defined as an irregular pattern composed of the contact gaps between the aggregate skeletons in the asphalt mixture.

[0079] Specifically, the extracted mixture microscopic characteristic parameters can be arranged in ascending order: the length of the longest axis of the mineral gap, the shortest axis of the mineral gap, and the length of the average axis of the mineral gap.

[0080] S4: Calculate the length distribution frequency of the skeleton characteristic parameters and fit the optimal length and optimal combination of fibers in the chopped fiber asphalt mixture.

[0081] Furthermore, the number of the longest axis of the ore gap, the shortest axis of the ore gap and the average axis of the ore gap in the interval (0, 1), [1, 2), [2, 3), ..., [n, +∞) are recorded as X1, X2, ..., X i , i is an integer from 1 to n.

[0082] It should be noted that the selection of characteristic parameters can guide the optimal design of fiber length combination in asphalt mixture from a microscopic perspective.

[0083] Example 2

[0084] Refer to Table 1-4, Figure 1-3 , which is an embodiment of the present invention, provides a fiber asphalt mixture skeleton characteristic parameter evaluation method based on digital technology. In order to verify its beneficial effects, the results are compared and verified in combination with actual practice.

[0085] The mix proportion of EA-10 epoxy asphalt mixture was designed.

[0086] The volume ratio of coarse aggregate in asphalt mixture, the volume of each grade of coarse and fine aggregate and the void ratio of mineral aggregate are calculated based on the mix design results.

[0087] The particle size of coarse aggregate is 2.36mm~13.2mm, the particle size of fine aggregate is 0mm~2.36mm, and the density of asphalt is r a =1.038g / cm 3 , coarse and fine aggregate density r b =2.7g / cm 3 , the total volume of asphalt mixture specimen V = 70 × 125 = 8750 mm 3 , the designed void ratio VV=2.5%, the volume of coarse and fine aggregate is v, and the oil-stone ratio a=7.5%. Then the mineral void ratio is:

[0088]

[0089] The passing rate and screening residue rate of each grade of coarse aggregate are shown in Table 1. The mass proportion of fine aggregate is 59.2%, the mass proportion of coarse aggregate is 40.8%, the total volume of coarse aggregate accounts for 33.8% of the total volume of the specimen, and the total volume of fine aggregate accounts for 49.1% of the total volume of the specimen. The ratio of each grade of coarse aggregate to the total volume of the specimen is shown in Table 1, and the ratio of each grade of fine aggregate to the total volume of the specimen is shown in Table 2.

[0090] Table 1 Volume parameters of coarse aggregate at different levels

[0091] Particle size / mm 13.2 9.5 4.75 2.36 Pass rate / % 100 98.7 74.5 59.2 Grading sieve residue / % 0 1.3 24.2 15.3 Volume share / % 0 1.1 20.0 12.7

[0092] Table 2 Parameters of fine aggregate of each grade

[0093] Particle size / mm 1.18 0.6 0.3 0.15 0.075 0 Pass rate / % 47.5 32.5 20.8 15.9 13.4 0 Sieve residue / % 11.7 15 11.7 4.9 2.5 13.4 Volume share / % 9.7 12.4 9.7 4.1 2.1 11.1

[0094] Construct the outline of the fine aggregate ball unit, and use different pebbles units to form the coarse aggregate clump template. Run the program to generate the EA-10 epoxy asphalt mixture discrete element model of the specified specimen size, such as Figure 3 shown.

[0095] Based on the discrete element microscopic model of EA-10 epoxy asphalt mixture, the image processing software Image-Pro-Plus (IPP) was used to extract the longest axis length of the mineral gap of EA-10 epoxy asphalt mixture and export the data.

[0096] The longest axis lengths of the gaps between aggregates in EA-10 epoxy asphalt mixture were extracted. The distribution of the longest axis lengths between aggregates was statistically determined to be (0, 1), [1, 2), [2, 3), …, [9, +∞), with proportions of X1 = 11.4%, X2 = 13.4%, X3 = 26.8%, X4 = 17.8%, X5 = 9.3%, X6 = 8.6%, X7 = 6.7%, X8 = 3.3%, and X9 = 2.7%, respectively. The maximum value (Xi) is Max(ni) = X3. The highest frequency distribution of the longest axis length between aggregates indicates that the optimal fiber length for EA-10 fiber asphalt mixture is 3 mm.

[0097] EA-10 basalt fiber epoxy asphalt mixture was prepared by selecting a 3 mm chopped fiber length, a fiber content of 0.3% of the total mass of the asphalt mixture, and an optimal asphalt-stone ratio of 7.7%. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 3 and 4.

[0098] Comparative Example 1:

[0099] The EA-10 basalt fiber epoxy asphalt mixture was prepared by selecting a 6 mm chopped fiber length, a total fiber content of 0.3% of the total mass of the asphalt mixture, and an optimal asphalt-stone ratio of 7.7%. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 3 and 4.

[0100] Comparative Example 2:

[0101] EA-10 basalt fiber epoxy asphalt mixture was prepared by selecting a 9 mm chopped fiber length, a total fiber content of 0.3% of the total mass of the asphalt mixture, and an optimal asphalt-stone ratio of 7.7%. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 3 and 4.

[0102] Table 3 Comparison of high temperature performance of epoxy asphalt mixtures with different lengths of chopped fibers

[0103] Experimental group <![CDATA[Deformation of t1 (mm)]]> <![CDATA[t2 deformation (mm)]]> Dynamic stability DS / times / mm Example 2 0.231 0.245 44798 Comparative Example 1 0.246 0.263 41580 Comparative Example 2 0.311 0.323 39437

[0104] Table 4 Comparison of low temperature performance of epoxy asphalt mixture with different lengths of chopped fiber

[0105]

[0106]

[0107] Example 3

[0108] Refer to Table 5-8, Figure 1-3 , which is another embodiment of the present invention, provides a fiber asphalt mixture skeleton characteristic parameter evaluation method based on digital technology. In order to verify its beneficial effects, another set of comparative verification is conducted on the results in combination with actual practice.

[0109] The mix proportion of AC-13 epoxy asphalt mixture was designed.

[0110] According to the mix design results, the volume ratio of coarse aggregate and the volume of each grade of coarse and fine aggregate in the asphalt mixture are calculated. The particle size of coarse aggregate is 2.36mm~13.2mm, the particle size of fine aggregate is 0.075mm~2.36mm, and the density of asphalt is r a =1.038g / cm 3 , coarse and fine aggregate density r b =2.7g / cm 3 , the total volume of asphalt mixture specimen V = 100 × 150 = 15000 mm 3 , the designed void ratio VV = 4%, the volume of coarse and fine aggregate is v, and the oil-stone ratio a = 5.2%. Then the mineral void ratio is:

[0111]

[0112] The passing rate and screening residue rate of each grade of coarse aggregate are shown in Table 5. The mass proportion of fine aggregate is 37%, the mass proportion of coarse aggregate is 63%, the total volume of coarse aggregate accounts for 53.7% of the total volume of the specimen, the total volume of fine aggregate accounts for 31.5% of the total volume of the specimen, and the proportion of each grade of fine aggregate to the total volume of the specimen is shown in Table 6.

[0113] Table 5 Parameters of coarse aggregate at each level

[0114] Particle size / mm 16 13.2 9.5 4.75 2.36 Pass rate / % 100 98.6 85.5 53.3 37.0 Sieve residue / % 0 1.4 13.1 32.2 16.3 Total proportion / % 0 1.2 11.2 27.4 13.9

[0115] Table 6 Parameters of fine aggregate of various grades

[0116] Particle size / mm 1.18 0.6 0.3 0.15 0.075 0 Pass rate / % 23.4 16.4 10.6 9.2 6.8 0 Sieve residue / % 13.6 7.0 5.8 1.4 2.4 6.8 Volume share / % 11.6 6.0 4.9 1.2 2.0 5.8

[0117] Construct the unit outline of fine aggregate ball, and use different pebbles units to form the coarse aggregate clump template. Run the program to generate the AC-13 asphalt mixture discrete element model of the specified specimen size, such as Figure 3 shown.

[0118] Based on the discrete element microscopic model of asphalt mixture, the image processing software Image-Pro-Plus (IPP) was used to extract the longest axis, shortest axis and average axis length of the aggregate gap of AC-13 asphalt mixture. The data were exported and an EXCEL table was generated.

[0119] The longest axis of the microscopic mineral gap is extracted, and the longest axis of the mineral gap is distributed in the interval (0, 1), [1, 2), [2, 3), ..., [9, +∞), with the proportions of X1 = 2.2%, X2 = 7.4%, X3 = 11.7%, X4 = 15.1%, X5 = 18.9%, X6 = 21.6%, X7 = 16.5%, X8 = 5.2%, X9 = 1.4%, and the maximum value of Xi (X i ) = X6. Based on the longest axis of the interstices between the aggregates, the optimal length of the chopped fibers in AC-13 asphalt mixture is 6 mm. The optimal fiber length combinations are 3 mm, 6 mm, and 9 mm. Under the optimal length combination, the mass ratio of the three fiber lengths is m3:m6:m9 = (X1+X2+X3):(X4+X5+X6):(X7+X8+X9) = 21.3:55.6:23.1≈2:5:2.

[0120] The chopped fiber length combinations of 3 mm, 6 mm, and 9 mm were selected, and their mass ratio was m3:m6:m9=2:5:2. The type of chopped fiber was basalt fiber, the total fiber content was 0.3% of the total mass of the asphalt mixture, and the optimal oil-stone ratio was 5.2%. AC-13 basalt fiber asphalt mixture was prepared. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 7 and 8.

[0121] Comparative Example 3:

[0122] The shortest axis of the microscopic mineral gap is extracted and the distribution of the shortest axis length of the mineral gap is statistically obtained in the interval (0, 1), [1, 2), [2, 3), ..., [9, +∞), with the proportions of X1 = 8.6%, X2 = 14.2%, X3 = 19.5%, X4 = 16.4%, X5 = 13.2%, X6 = 11.4%, X7 = 9.3%, X8 = 4.7%, X9 = 2.7%, and the maximum value of Xi (X i) = X3. Based on the shortest axis of the interstices between the aggregates, the optimal length of the chopped fibers in the asphalt mixture is 3 mm. The optimal fiber length combinations are 3 mm, 6 mm, and 9 mm. Under the optimal length combination, the mass ratio of the three fiber lengths is m3:m6:m9 = (X1+X2+X3):(X4+X5+X6):(X7+X8+X9) = 37.2:43.1:19.7 ≈ 2:2:1.

[0123] The chopped fiber length combinations of 3 mm, 6 mm, and 9 mm were selected, and their mass ratio was m3:m6:m9=2:2:1. The type of chopped fiber was basalt fiber, the total fiber content was 0.3% of the total mass of the asphalt mixture, and the optimal asphalt-stone ratio was 5.2%. AC-13 basalt fiber asphalt mixture was prepared. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 7 and 8.

[0124] Comparative Example 4:

[0125] The average axis of the microscopic mineral gap is extracted and the average axis length of the mineral gap is statistically distributed in the interval (0, 1), [1, 2), [2, 3), ..., [9, +∞), with the proportions of X1 = 9.3%, X2 = 10.3%, X3 = 13.8%, X4 = 7.9%, X5 = 10.3%, X6 = 16.9%, X7 = 13.6%, X8 = 12.4%, X9 = 5.5%, and the maximum value of Xi (X i )=X6. Based on the average axis of the mineral gap, the optimal mixing length of chopped fiber in asphalt mixture is 6 mm. The optimal fiber length combination is 3 mm, 6 mm, and 9 mm. Under the optimal length combination, the mass ratio of the three length fibers is m3:m6:m9=(X1+X2+X3):(X4+X5+X6):(X7+X8+X9)=33.4:35.1:31.5≈1:1:1.

[0126] The chopped fiber length combinations of 3 mm, 6 mm, and 9 mm were selected, with a mass ratio of m3:m6:m9=1:1:1. The type of chopped fiber was basalt fiber, the total fiber content was 0.3% of the total mass of the asphalt mixture, and the optimal asphalt-stone ratio was 5.2%. AC-13 basalt fiber asphalt mixture was prepared. The high-temperature rutting test and low-temperature beam bending test were used to test its high-temperature performance and low-temperature performance, respectively. The results are shown in Tables 7 and 8.

[0127] Table 7 Comparison of high temperature performance of AC-13 asphalt mixture with different length combinations of chopped fibers

[0128] Experimental group Dynamic stability DS / times / mm Example 3 4967 Comparative Example 3 4767 Comparative Example 4 4574

[0129] Table 8 Comparison of low temperature performance of AC-13 asphalt mixture with different lengths of chopped fibers

[0130]

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology, characterized in that: include: According to the fiber asphalt mixture ratio results, the volume ratio of coarse and fine aggregates in the fiber asphalt mixture is obtained; Irregular polygonal particles are constructed, and multiple "rigid cluster" templates of asphalt mixture coarse aggregate are constructed based on discrete element technology. The "rigid cluster" templates are used to generate a two-dimensional discrete element mesoscopic model of the fiber asphalt mixture aggregate gradation under the real contour. The shape of the asphalt mixture coarse aggregate is similar to an irregular polygon with 4 to 10 sides; In a two-dimensional polar coordinate system, the shape parameters of the polygon include the center of the polygon (X i , Y i ), the number of sides n of the polygon, the polar radius r of the i-th side of the polygon i and the polar angle θ of the i-th side of the polygon r； The shape and size parameters of the polygon are determined by a random number N i control; Among them, X max , X min , Y max , Y min are the maximum abscissa, minimum abscissa, maximum ordinate and minimum ordinate of the polygonal two-dimensional section; The polygonal particle size is [D s ,D s+1 ]; X coordinate of polygon center i =X min +N i ×(X max -X min ); Y coordinate of the polygon center i =Y min +N i ×(Y max -Y min ); Number of polygon sides n = 4 + N i ×(10-4); Polar diameter Polar angle Performing image processing on a two-dimensional discrete element microscopic model of asphalt mixture aggregate gradation to extract skeleton characteristic parameters of the asphalt mixture, including the longest axis of the aggregate gap, the shortest axis of the aggregate gap, and the average axis length of the aggregate gap; The length distribution frequency of the skeleton characteristic parameters is calculated to fit the optimal length and optimal combination of fibers in the chopped fiber asphalt mixture.

2. The method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to claim 1, characterized in that: The discrete element technology is used to construct multiple asphalt mixture coarse aggregate "rigid cluster" templates. include, First, the true contour of the coarse aggregate of the asphalt mixture is fitted by "cluster" particles, specifically including: The "rigid cluster" template of asphalt mixture coarse aggregate is formed by superimposing "cluster" units with different poles and polar axes.

3. The method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to claim 2, characterized in that: The number of the "cluster" units is greater than 10, and the size of the "cluster" units ranges from 1 mm to 3 mm.

4. The method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to claim 3, characterized in that: Before generating the two-dimensional discrete element mesoscopic model of fiber asphalt mixture aggregate gradation under the real contour through the "rigid cluster" template, it includes: Within the range of the specimen mold, various circular coarse aggregate and fine aggregate units are generated according to the aggregate gradation, and the "rigid cluster" contour template library program is loaded for each circular coarse aggregate.

5. The method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to claim 4, characterized in that: The coarse aggregate is characterized by rigid cluster particles, and the fine aggregate particles are characterized by spherical unit particles.

6. The method for evaluating characteristic parameters of fiber asphalt mixture skeleton based on digital technology according to claim 5, characterized in that: The skeleton characteristic parameters are obtained from the aggregate gap distribution diagram of the asphalt mixture. The asphalt mixture aggregate gap distribution diagram is defined as an irregular graph composed of the aggregate skeleton contact gaps in the asphalt mixture.

7. The method for evaluating fiber asphalt mixture skeleton characteristic parameters based on digital technology according to claim 6, characterized in that: The number of the longest axis of the ore gap, the shortest axis of the ore gap and the average axis of the ore gap in the interval (0, 1), [1, 2), [2, 3), ..., [n, +∞) is recorded as X1, X2, ..., X i , i is an integer from 1 to n.

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