An assembled elastic solidified track bed aggregate grading design method

Abstract

The application discloses a kind of assembly type elastic solidification track bed aggregate grading design method, establishes the track ball grading fractal model of elastic solidification track bed;For ballast particle setting several different particle size ranges, respectively into track ball grading fractal model, and give the track ball grading fractal model linear fractal dimension, obtain first grading curve group;Optimal grading curve is screened in first grading curve group, and particle size range is extracted;The extracted particle size range is substituted into the track ball grading fractal model, and the track ball grading fractal model is set to several different fractal dimensions, to obtain second grading curve group;Optimal grading curve is screened in second grading curve group as final design result.The application is used to solve the problem that there is no special grading design method for assembly type elastic solidification track bed in the prior art, to realize the purpose of scientifically and reasonably quantifying grading from multiple dimensions for the grading design of elastic solidification track bed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit, in particular to a method for designing aggregate gradation of assembled elastic solidified track bed. BACKGROUND

[0002] As an innovative track structure, the elastic solidified track bed adopts polyurethane foam material as the core, which foams in the granular bed of the ballast track, fills and solidifies the voids between the ballast particles, and forms a wrapped and stable structure. This structure effectively avoids the hard contact between the ballast particles and the possible dislocation and slip, thereby significantly slowing down the pulverization and deterioration process of the ballast and greatly enhancing the stability and durability of the track bed structure. The polyurethane solidified track bed not only inherits the advantage of easy adjustment of the ballast track, but also has the characteristics of low maintenance and high stability of the non-ballast track. Therefore, as the third track structure form after the ballast track and the non-ballast track, it has shown its unique advantages and value in the field of track construction.

[0003] The selection of gradation is crucial in the preparation of the track bed. Due to improper selection of gradation, various problems may occur in the track bed during use, such as settlement, deformation, cracking, etc., thereby increasing the maintenance cost and difficulty.

[0004] For the polyurethane solidified track bed, the gravel particles are wrapped by polyurethane material to form a stable structure, which is significantly different from the traditional granular bed. However, the commonly used track bed gradation design methods at home and abroad do not consider this particularity of the polyurethane solidified track bed. Moreover, in the existing selection process of ballast gradation, the focus is mainly on the running speed of the line or the total annual passing weight, and this selection method often ignores the mechanical properties, economy, etc. of the track bed, and cannot effectively guide the gradation design of the polyurethane solidified track bed from multiple angles, and has low applicability in the selection of the gradation of the polyurethane solidified track bed.

[0005] In summary, there is still a lack of a comprehensive method for scientifically and reasonably designing the gradation of the assembled elastic solidified track bed in the prior art. SUMMARY

[0006] The present application provides a method for designing the aggregate gradation of the assembled elastic solidified track bed, to solve the problem of the lack of a method for designing the gradation of the assembled elastic solidified track bed in the prior art, and to achieve the purpose of scientifically and reasonably quantifying the gradation from multiple dimensions.

[0007] The present application is achieved by the following technical scheme:

[0008] A method for designing the aggregate gradation of the assembled elastic solidified track bed, comprising:

[0009] A fractal model of ballast gradation of the elastic stabilized track bed is established;

[0010] A plurality of different particle size ranges are set for the ballast particles;

[0011] The plurality of different particle size ranges are respectively substituted into the fractal model of ballast gradation, and a linear fractal dimension is given to the fractal model of ballast gradation, so as to obtain a plurality of gradation curves, which are defined as a first gradation curve group; the linear fractal dimension is a fractal dimension that makes the obtained gradation curve a straight line.

[0012] An optimal gradation curve is screened from the first gradation curve group, which is defined as a first gradation curve, and a particle size range corresponding to the first gradation curve is extracted;

[0013] The particle size range corresponding to the first gradation curve is substituted into the fractal model of ballast gradation, and a plurality of different fractal dimensions are set for the fractal model of ballast gradation, so as to obtain a plurality of gradation curves, which are defined as a second gradation curve group;

[0014] An optimal gradation curve is screened from the second gradation curve group, which is defined as a second gradation curve, and the second gradation curve is taken as a final design result.

[0015] In view of the problem that there is no gradation design method specifically for the assembled elastic stabilized track bed in the prior art, the present application provides an aggregate gradation design method for the assembled elastic stabilized track bed. The method first establishes a fractal model of ballast gradation of the elastic stabilized track bed based on the concept of fractal dimension. Then, the upper and lower limits of the gradation of the ballast particles to be designed are determined. The determination method is as follows: first, a plurality of different particle size ranges are set for the ballast particles, i.e., a plurality of different maximum particle sizes and minimum particle sizes are set, then the different particle size ranges are respectively substituted into the fractal model of ballast gradation, and a linear fractal dimension is taken in the fractal model of ballast gradation, then the gradation curves corresponding to the different particle size ranges are obtained, and all the gradation curves obtained at this time are collectively defined as a first gradation curve group; the setting of the plurality of different particle size ranges can be based on experience, the prior art or relevant standards, etc., and each particle size range corresponds to a gradation curve. Then, the method screens an optimal gradation curve from the first gradation curve group, takes the screened optimal gradation curve as a first gradation curve, extracts the particle size range corresponding to the first gradation curve, and the upper and lower limits of the gradation of the ballast particles to be designed can be obtained based on the extracted particle size range.

[0016] Then, the fractal dimension is determined: the particle size range corresponding to the first gradation curve is substituted into the fractal model of ballast gradation, a plurality of different fractal dimensions are set for the fractal model of ballast gradation, gradation curves corresponding to the different fractal dimensions are obtained, all the gradation curves obtained at this time are collectively defined as a second gradation curve group, then an optimal gradation curve is screened from the second gradation curve group, which is defined as a second gradation curve, and the second gradation curve is taken as a final design result output.

[0017] The application creatively introduces the fractal dimension concept in the gradation design process of the elastic stabilized ballast bed, and the gradation design of the elastic stabilized ballast bed is completed through two screenings based on the ballast gradation fractal model. The designed gradation curve is extremely strong for the elastic stabilized ballast bed, and fills the gap in the prior art.

[0018] Further, the ballast gradation fractal model of the elastic stabilized ballast bed is:

[0019]

[0020] r min = alpha * r max

[0021] In the formula, Y(r) is the mass percentage of ballast particles passing through the square hole screen with a hole length of r; C f is the ballast contamination rate of the ballast bed; D is the fractal dimension; r max is the maximum particle size of the ballast particles; r min is the minimum particle size of the ballast particles; and alpha is the screen hole coefficient.

[0022] The ballast gradation fractal model of the present application can be applied to the ballast gradation of the elastic stabilized ballast bed at different operation stages, and has strong universality and good adaptability.

[0023] Preferably, in the process of obtaining the first group of gradation curves, the ballast contamination rate C f = 0; and the straight line fractal dimension is D = 1.

[0024] In the process of obtaining the first group of gradation curves, C f = 0 and D = 1 are taken in the ballast gradation fractal model, so as to ensure that all the gradation curves in the first group of gradation curves are straight lines.

[0025] Preferably, in the process of obtaining the second group of gradation curves, a plurality of different fractal dimensions are set for the ballast gradation fractal model, and the plurality of different fractal dimensions are all taken in [-10, 10].

[0026] Further, the method for screening the optimal gradation curve comprises:

[0027] determining the following evaluation indexes: a mechanical property index, a durability index, a polyurethane distribution uniformity index, an economic index and a process stability index;

[0028] based on the screened gradation curve, carrying out a physical test and / or simulation of the elastic stabilized ballast bed to obtain the values of the evaluation indexes;

[0029] Based on the grey correlation degree method, the grey correlation degrees of the grading curves at all levels under all evaluation indexes are calculated.

[0030] The grading curve with the highest grey correlation degree is taken as the optimal grading curve.

[0031] It should be noted that the method for screening the optimal grading curve can be used to screen the optimal grading curve in the first grading curve group and the second grading curve group.

[0032] In the process of screening the optimal grading curve, the evaluation indexes under different angles are determined from the mechanical properties, durability, polyurethane distribution uniformity, economy and process stability, then for each grading curve participating in the screening, the values of the evaluation indexes are obtained in the form of physical test and / or simulation simulation according to the corresponding particle size distribution characteristics of the gravel, and the grey correlation degrees of the grading curves at all levels under all evaluation indexes are calculated by the grey correlation degree method, the grey correlation degrees corresponding to all grading curves are compared, and the grading curve with the highest grey correlation degree is taken as the optimal grading curve screened this time.

[0033] It can be seen that the present scheme comprehensively evaluates the grading design of the elastic stabilized bed, quantitatively evaluates the reasonable grading from multiple aspects, significantly improves the accuracy of the grading design of the elastic stabilized bed, and ensures that the designed structure is more scientific and reasonable. The existing grey correlation degree method can be used to calculate the grey correlation degree, and details are not repeated here.

[0034] Preferably,

[0035] The mechanical property index includes: bearing capacity of the test piece;

[0036] The durability index includes: the rate of change of the static modulus of the test piece with the number of load actions, the slope of the load-displacement curve of the test piece after deformation stabilization, the deformation recovery rate of the test piece within a specified time after unloading, the energy dissipation capacity and the cooperative bearing performance;

[0037] The polyurethane distribution uniformity index includes: the spatial distribution variation coefficient of porosity;

[0038] The economy index includes: the porosity of the test piece;

[0039] The process stability index includes: the expansion force of polyurethane.

[0040] The present scheme specifically defines the specific parameters corresponding to each evaluation index. The durability index includes at least five parameters, which is especially suitable for the grading selection of the elastic stabilized bed.

[0041] Further, the mechanical performance index is obtained by the following method: based on the screened grading curve, a test piece is prepared, uniaxial compression experiment is carried out on the test piece, and the load-displacement curve is recorded, and the load at the yield point of the test piece is taken as the bearing capacity of the test piece.

[0042] The polyurethane distribution uniformity index is obtained by the following method: based on the screened grading curve, a three-dimensional model of the elastic solidified ballast bed is established, the model is divided into a plurality of spaces with the same length, width and height, the porosity of each space is calculated respectively, and the porosity spatial distribution variation coefficient is calculated by the following formula:

[0043]

[0044] In the formula, C v is the porosity spatial distribution variation coefficient; sigma is the standard deviation of the porosity of all spaces; u is the average value of the porosity of all spaces; rho i is the porosity of the i-th space; n is the total number of divided spaces.

[0045] The economic index is obtained by the following method: based on the screened grading curve, a test piece is prepared, and the porosity of the test piece is measured.

[0046] The process stability index is obtained by the following method: a mold is prepared, a pressure sensor is installed on the inner wall of the mold top cover; based on the screened grading curve, ballast particles are filled into the mold; polyurethane is poured into the mold; the mold top cover is closed, and the maximum stress of the pressure sensor during the expansion of the polyurethane is recorded as the expansion force of the polyurethane.

[0047] The scheme clearly defines the specific obtaining method of the mechanical performance index, the polyurethane distribution uniformity index, the economic index and the process stability index; wherein the mechanical performance index, the economic index and the process stability index are obtained by the way of physical test, and the polyurethane distribution uniformity index is obtained by the way of modeling.

[0048] Further, the obtaining method of the durability index comprises:

[0049] Based on the screened grading curve, a test piece is prepared;

[0050] Under the simulation of heavy load working conditions, a total of N times of cyclic load is applied to the test piece; during the period, after every M times of cyclic load is applied, the test piece is left to stand for a set time, and the static modulus is measured; wherein M < 1 / 2N;

[0051] In units of M times, the change curve of the static modulus with the number of load actions is drawn, and linear fitting is carried out, and the slope of the straight line obtained by linear fitting is taken as the change rate of the static modulus of the test piece with the number of load actions;

[0052] Draw the curve of vertical deformation of the test piece with the number of load action in M times, and linearly fit the part after deformation stabilization, so that the slope of the linear fitting is the slope of the load-displacement curve of the test piece after deformation stabilization;

[0053] In the process of determining the static modulus, the deformation recovery of the test piece at a specified time after unloading is recorded, and the deformation recovery rate of the test piece at a specified time after unloading is obtained by dividing the deformation recovery by the maximum deformation of the test piece.

[0054] Preferably, N and M are both in units of ten thousand times.

[0055] The method for determining the static modulus comprises:

[0056] Load the test piece at a constant rate of 1mm / min, unload to 0kN after loading to 30kN, and repeat twice;

[0057] Load the test piece at a constant rate of 1mm / min for the third time, record the load value and displacement curve until the load reaches 30kN;

[0058] Unload, and continue to record the displacement 30min after unloading;

[0059] Calculate the static modulus based on the third loading:

[0060]

[0061] In the formula: C i The static modulus F, the upper limit load F1 for calculating the static modulus, the lower limit load F0 for calculating the static modulus, the area of the loading plate A, the displacement D1 when the load value is F1, and the displacement D0 when the load value is F0.

[0062] Further, the synergistic bearing performance is obtained by the following method:

[0063] Based on the screened grading curve, a simulation model of the elastic solidified roadbed is established;

[0064] Simulation is performed to obtain the force borne by the polyurethane material and the force borne by the gravel aggregate in the model under a specified load action;

[0065] The synergistic bearing performance Rf is calculated, and Rf=Fp / Fb; in the formula: Rf is the synergistic bearing performance; Fp is the force borne by the polyurethane material; and Fb is the force borne by the gravel aggregate.

[0066] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0067] 1. The assembly type elastic solidified track bed aggregate grading design method creatively introduces the fractal dimension concept in the grading design process of the elastic solidified track bed, completes the grading design of the elastic solidified track bed through two screenings based on the track ballast fractal model, and the designed grading curve is extremely strong for the elastic solidified track bed, and fills the gap of the prior art.

[0068] 2. The assembly type elastic solidified track bed aggregate grading design method, the track ballast fractal model can be applied to the elastic solidified track bed ballast grading at different operation stages, has extremely strong universality and good adaptability.

[0069] 3. The assembly type elastic solidified track bed aggregate grading design method is used for comprehensively evaluating the grading design of the elastic solidized track bed, quantitatively evaluating the reasonable grading from multiple aspects, significantly improving the accuracy of the elastic solidified track bed grading design, and ensuring that the designed structure is more scientific and reasonable. DETAILED DESCRIPTION

[0070] The accompanying drawings used to provide further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation to the embodiments of the present application. In the drawings:

[0071] Figure 1 is a flowchart of the specific embodiment of the present application;

[0072] Figure 2 is a schematic view of the first grading curve group in the specific embodiment of the present application;

[0073] Figure 3 is a schematic view of the loading device in the specific embodiment of the present application;

[0074] Markings in the drawings and corresponding names of parts:

[0075] 1 - test piece, 2 - loading plate, 3 - displacement sensor, 4 - pressure sensor, 5 - loading head, 6 - counterforce frame. DETAILED DESCRIPTION

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explaining the invention only and are not intended to limit the invention. In the description of this application, it should be understood that terms such as "front," "rear," "left," "right," "upper," "lower," "vertical," "horizontal," "high," "low," "inner," and "outer," indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this application.

[0077] Example 1:

[0078] like Figure 1 The method for designing aggregate gradation for prefabricated elastic curing track bed includes the following steps:

[0079] S1. Establish a fractal model of ballast gradation for elastically cured track bed:

[0080]

[0081] r min =α×r max

[0082] In the formula: Y(r) is the percentage of ballast particles passing through a square-hole sieve with a side length of r; C f D represents the track bed dirt rate; D represents the subdimension; r max r represents the maximum particle size of ballast. min α represents the minimum particle size of ballast; α is the sieve aperture coefficient.

[0083] As can be seen, in the ballast gradation fractal model of this embodiment, the gradation curve is determined after the upper and lower limits of particle size and the fractal dimension are determined.

[0084] S2. Because the elastic curing track bed is assembled in the factory, the particles within a certain range under the pillow are vibrated and compacted in a sealed container before the elastic curing material is poured in for curing. This approximately rectangular curing range is smaller than the entire track bed, and excessively large particle sizes as specified in the original specifications may make it difficult for the cured blocks to compact. Therefore, it is necessary to first determine the range of particle sizes.

[0085] First, several different particle size ranges were set for the ballast particles:

[0086] The embodiment is designed for 10 different particle size ranges with maximum particle sizes of 63 mm, 56 mm, 50 mm, 45 mm, 40 mm, 35.5 mm, and 31.5 mm, and minimum particle sizes of 16 mm, 10 mm, or 5 mm, respectively. The specific design scheme is shown in Table 1.

[0087] Table 1

[0088]

[0089]

[0090] S3, a plurality of different particle size ranges are substituted into the ballast fractal model, the ballast contamination rate C f = 0 is taken, and a straight line fractal dimension is given to the ballast fractal model to obtain a plurality of grading curves, defined as a first grading curve group, as shown in Figure 2 ; wherein the straight line fractal dimension is a fractal dimension that makes the obtained grading curve a straight line; in this embodiment, the straight line fractal dimension is D = 1.

[0091] It is found through comparison that the grading A belongs to the American AREMA Railway Engineering Manual No. 24 grading, the grading B is similar to the American AREMA Railway Engineering Manual No. 24A grading, the gradings C to G are similar to the European Gc RB E grading, and the gradings H, I, and J have smaller particle sizes and are quite different from the above-mentioned railway ballast gradings, but are similar to the continuous grading 5-40 in the Chinese “Construction Pebble, Gravel” (GB / T 14685-2022) particle grading.

[0092] S4, the optimal grading curve is screened from the first grading curve group, defined as a first grading curve, and the particle size range corresponding to the first grading curve is extracted;

[0093] S5, the particle size range corresponding to the first grading curve is substituted into the ballast fractal model, and a plurality of different fractal dimensions in the interval [-10, 10] are set for the ballast fractal model to obtain a plurality of grading curves, defined as a second grading curve group;

[0094] S6, the optimal grading curve is screened from the second grading curve group, defined as a second grading curve, and the second grading curve is taken as the final design result.

[0095] Embodiment 2:

[0096] A prefabricated elastic stabilized ballast aggregate grading design method, based on embodiment 1, in steps S4 and S5, the optimal grading curve is screened by the following method:

[0097] I. Determine the following evaluation indexes: mechanical property index, durability index, polyurethane distribution uniformity index, economic index and process stability index. Specifically:

[0098] The mechanical property index includes: the bearing capacity F of the test piece;

[0099] The durability index includes: the rate of change of the static modulus of the test piece with the number of load applications Km, the slope of the load-displacement curve of the test piece after deformation stabilization Ks, the deformation recovery rate of the test piece within a specified time after unloading Rr, the energy dissipation capacity E and the collaborative bearing performance Rf;

[0100] The polyurethane distribution uniformity index includes: the spatial distribution variation coefficient Cv of porosity;

[0101] The economic index includes: the porosity of the test piece p;

[0102] The process stability index includes: the expansion force Ff of the polyurethane.

[0103] II. Based on the screened grading curve, carry out physical test and / or simulation of elastic solidified roadbed to obtain the values of each evaluation index. Specifically:

[0104] 1) Mechanical property index: based on the screened grading curve, make test pieces, and carry out uniaxial compression experiment on the test pieces, record the load-displacement curve, and take the load at the yield point of the test piece as the bearing capacity F of the test piece;

[0105] 2) Each durability index:

[0106] Based on the screened grading curve, make test pieces;

[0107] Simulate heavy load working conditions, apply a total of 100,000 cycles of load to the test piece with a load amplitude of 22.5kN; the loading device is as shown in Figure 3 ;

[0108] During this period, after applying each 10,000 cycles of load, the test piece is left for 1 hour, and the static modulus is measured; the method for measuring the static modulus includes:

[0109] Load the test piece at a rate of 1mm / min, load to 30kN and unload to 0kN, repeat twice;

[0110] Load the test piece at a rate of 1mm / min for the third time, record the load value and displacement curve respectively, until the load reaches 30kN;

[0111] Unload, continue to record the displacement 30 minutes after unloading;

[0112] Calculate the static modulus based on the third loading:

[0113]

[0114] In the formula: C i Static modulus; F1 is the upper limit load for calculating the static modulus; F0 is the lower limit load for calculating the static modulus; A is the loading plate area; D1 is the displacement when the loading value is F1; D0 is the displacement when the loading value is F0.

[0115] Draw the curve of the static modulus with the number of load actions in units of 10,000 times, and perform linear fitting. The slope of the linear fitting is taken as the rate of change of the static modulus of the test piece with the number of load actions Km.

[0116] Draw the curve of the vertical deformation of the test piece with the number of load actions in units of 10,000 times, and perform linear fitting on the part after deformation stabilization (for example, 5-10 million times). The slope of the linear fitting is taken as the slope of the load-displacement curve after deformation stabilization of the test piece Ks.

[0117] During the determination of the static modulus, record the deformation recovery of the test piece 30 minutes after unloading. Divide the deformation recovery by the maximum deformation of the test piece to obtain the deformation recovery rate Rr of the test piece within a specified time after unloading.

[0118] In addition, the energy dissipation capacity E is represented by the area enclosed by the force-displacement curve, which can be obtained by reference to JGJT101-2015 "Building Seismic Test Code".

[0119] In addition, the collaborative bearing performance Rf is obtained by the following method:

[0120] Based on the screened grading curve, a simulation model of the elastic stabilized roadbed is established;

[0121] Simulation is performed to obtain the force on the polyurethane material and the force on the gravel aggregate in the model under a specified load.

[0122] The collaborative bearing performance Rf is calculated: Rf = Fp / Fb; in the formula: Rf is the collaborative bearing performance; Fp is the force on the polyurethane material; Fb is the force on the gravel aggregate.

[0123] 3) Polyurethane uniformity index:

[0124] Based on the screened grading curve, a three-dimensional model of the elastic stabilized roadbed is established. The model is divided into a plurality of spaces with the same length, width, and height. The porosity of each space is calculated, and the porosity spatial distribution coefficient of variation is calculated by the following formula:

[0125]

[0126] In the formula: C vis the coefficient of variation of the spatial distribution of porosity; σ is the standard deviation of porosity of all spaces; u is the average value of porosity of all spaces; ρ i is the porosity of the i-th space in the model; n is the total number of divided spaces;

[0127] 4) Economic index:

[0128] Based on the screened grading curve, a test piece is made, and the porosity ρ of the test piece is measured by the following formula:

[0129]

[0130] wherein, m 颗粒 is the total mass of ballast particles in the test piece, which is obtained by weighing in the actual test piece production process; ρ 颗粒 is the bulk density of ballast particles, which is obtained by testing in TB / T 2140.2-2018; V 颗粒 is the volume of ballast particles; V 模具 is the volume of the mold.

[0131] 5) Process stability index:

[0132] Prepare the mold, install the pressure sensor on the inner wall of the mold top cover; fill the ballast particles into the mold based on the screened grading curve; pour polyurethane into the mold; close the mold top cover, and record the maximum force of the pressure sensor during the expansion of the polyurethane as the expansion force Ff of the polyurethane.

[0133] III. Based on the grey correlation degree method, the grey correlation degrees of each grading curve under all evaluation indexes are calculated;

[0134] First, the reference series and comparison series are set for n evaluation indexes and m gradings;

[0135] Then, the index values are normalized;

[0136] Then, the grey correlation coefficients are calculated;

[0137] Finally, the grey correlation degree is calculated: In the formula: γ(X0(k),X i (k)) represents the grey correlation degree corresponding to the k-th grading; ω i represents the weight coefficient of the i-th index, which satisfies

[0138] In this embodiment, the weight coefficients of each index are shown in Table 2:

[0139] Table 2 Weight coefficients

[0140] Indicator F Km Ks Rr E Rf Cv p Ff Weight coefficient 0.1 0.12 0.15 0.15 0.1 0.08 0.1 0.1 0.1

[0141] Four, the highest gray correlation degree scheme corresponding grading curve as the optimal grading curve.

[0142] The above detailed description of the specific embodiments of the present application, the purpose, technical solutions and beneficial effects have been further detailed, it should be understood that the above description is only a specific embodiment of the present application, and is not intended to limit the scope of protection of the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the scope of protection of the present application.

[0143] It should be noted that in this paper, such as the first and second relationship terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. In addition, the term "connected" used in this paper can be directly connected or indirectly connected via other components without special description.

Claims

1. A method for designing aggregate gradation for prefabricated elastic curing track bed, characterized in that, include: Establish a fractal model of ballast gradation for elastically cured track bed; Several different particle size ranges are set for ballast particles; Several different particle size ranges are substituted into the ballast gradation fractal model, and a linear fractal dimension is assigned to the ballast gradation fractal model to obtain several gradation curves, which are defined as the first gradation curve group; wherein, the linear fractal dimension is the fractal dimension that makes the obtained gradation curves straight lines. The optimal gradation curve is selected from the first gradation curve group and defined as the first gradation curve. The particle size range corresponding to the first gradation curve is then extracted. Substitute the particle size range corresponding to the first gradation curve into the ballast gradation fractal model, and set several different fractal dimensions for the ballast gradation fractal model to obtain several gradation curves, which are defined as the second gradation curve group. The optimal gradation curve is selected from the second gradation curve group and defined as the second gradation curve. The second gradation curve is used as the final design result.

2. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 1, characterized in that, The fractal model of the ballast gradation of the elastically cured track bed is as follows: r min ＝α×r max In the formula: Y(r) is the percentage of ballast particles passing through a square-hole sieve with a side length of r; C f D represents the track bed dirt rate; D represents the subdimension; r max r represents the maximum particle size of ballast. min α represents the minimum particle size of ballast; α is the sieve aperture coefficient.

3. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 2, characterized in that, In obtaining the first set of gradation curves: The track bed dirt rate C is taken. f =0; the dimension of the line is: D=1.

4. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 1, characterized in that, In the process of obtaining the second set of gradation curves, several different subdimensions take values ​​between [-10, 10].

5. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 1, characterized in that, The method for selecting the optimal gradation curve includes: The following evaluation indicators were determined: mechanical performance indicators, durability indicators, polyurethane distribution uniformity indicators, economic indicators, and process stability indicators. Based on the selected gradation curves, physical tests and / or simulations of the elastic curing track bed were conducted to obtain the values ​​of each evaluation index. Based on the grey relational analysis method, the grey relational degree of each level of matching curve is calculated under all evaluation indicators; The gradation curve with the highest grey relational degree is taken as the optimal gradation curve.

6. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 5, characterized in that, The mechanical performance indicators include: specimen load-bearing capacity; The durability indicators include: the rate of change of the static modulus of the specimen with the number of loads, the slope of the load-displacement curve after the specimen has stabilized in deformation, the deformation recovery rate of the specimen within a specified time after unloading, energy dissipation capacity, and synergistic load-bearing performance. The polyurethane distribution uniformity index includes: porosity spatial distribution variation coefficient; The economic indicators include: specimen porosity; The process stability index includes the expansion force of polyurethane.

7. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 6, characterized in that, The mechanical performance indicators are obtained by the following method: based on the selected gradation curve, specimens are made, uniaxial compression tests are performed on the specimens, load-displacement curves are recorded, and the load at the yield point of the specimen is taken as the bearing capacity of the specimen. The polyurethane distribution uniformity index is obtained as follows: Based on the selected gradation curve, a three-dimensional model of the elastically cured track bed is established. The model is divided into several spaces with the same length, width, and height. The porosity of each space is calculated, and the porosity spatial distribution variation coefficient is calculated using the following formula: In the formula: C v ρ is the coefficient of variation of porosity spatial distribution; σ is the standard deviation of porosity in all spaces; u is the average porosity in all spaces; ρ i Let be the porosity of the i-th space; n is the total number of spaces divided. The economic indicators are obtained by the following method: preparing specimens based on the selected gradation curve and measuring the porosity of the specimens; The process stability index is obtained by the following method: prepare a mold and install a pressure sensor on the inner wall of the mold top cover; fill the mold with ballast particles based on the selected gradation curve; pour polyurethane into the mold; close the mold top cover and record the maximum force on the pressure sensor during the polyurethane expansion process as the expansion force of the polyurethane.

8. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 6, characterized in that, The method for obtaining the durability index includes: Specimens were prepared based on the selected gradation curves; Simulate heavy load conditions and apply a total of N cycles of load to the specimen; during this period, after every M cycles of load application, the specimen is left to stand for a set time and the static modulus is measured; where M < 1 / 2N; Plot the static modulus as a function of the number of load cycles in units of M cycles, and perform linear fitting. Use the slope of the straight line obtained by linear fitting as the rate of change of the static modulus of the specimen with the number of load cycles. Using M load cycles as the unit, plot the curve of vertical deformation of the specimen as a function of the number of load cycles. Perform linear fitting on the part after deformation stabilization, and use the slope of the straight line obtained by linear fitting as the slope of the load-displacement curve after the specimen deformation stabilization. During the determination of static modulus, the deformation recovery of the specimen after unloading at a specified time is recorded. The deformation recovery is divided by the maximum deformation of the specimen to obtain the deformation recovery rate of the specimen after unloading within a specified time.

9. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 8, characterized in that, Methods for determining static modulus include: The specimen was loaded at a constant speed of 1 mm / min, and then unloaded to 0 kN after reaching 30 kN. This process was repeated twice. The specimen was loaded a third time at a constant speed of 1 mm / min, and the loading value and displacement curve were recorded respectively, until the loading reached 30 kN; Uninstall, and continue recording the displacement for 30 minutes after uninstallation; Calculate the static modulus based on the third loading: In the formula: C i F1 is the static modulus; F0 is the upper limit load for calculating the static modulus; A is the area of ​​the loading plate; D1 is the displacement when the loading value is F1; D0 is the displacement when the loading value is F0.

10. The method for aggregate gradation design of prefabricated elastic curing track bed according to claim 6, characterized in that, The cooperative load-bearing capacity is obtained through the following method: A simulation model of the elastically cured track bed was established based on the selected gradation curves. The simulation was conducted to obtain the forces exerted on the polyurethane material and the crushed stone aggregate in the model under a specified load. Calculate the synergistic load-bearing capacity: Rf = Fp / Fb; where: Rf is the synergistic load-bearing capacity; Fp is the force on the polyurethane material; Fb is the force on the crushed stone aggregate.

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